Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
camera-based Lane detection by deep learningYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
Vehicle Detection using Camera
Vehicle Detection Using Cameras for Self-Driving Cars |
Using machine learning and computer vision I create a pipeline that detects nearby vehicles from a dash-cam.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
camera-based Lane detection by deep learningYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
Vehicle Detection using Camera
Vehicle Detection Using Cameras for Self-Driving Cars |
Using machine learning and computer vision I create a pipeline that detects nearby vehicles from a dash-cam.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model
CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network
CFNet: LiDAR-Camera Registration Using Calibration Flow Network
Goal location prediction based on deep learning using RGB-D camerajournalBEEI
In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
3D perception is crucial for understanding the real world. It offers many benefits and new capabilities over 2D across diverse applications, from XR and autonomous driving to IOT, camera, and mobile. 3D perception with machine learning is creating the new state of the art (SOTA) in areas, such as depth estimation, object detection, and neural scene representation. Making these SOTA neural networks feasible for real-world deployment on mobile devices constrained by power, thermal, and performance has been a challenge. Qualcomm AI Research has developed not only novel AI techniques for 3D perception but also full-stack AI optimizations to enable real-world deployments and energy-efficient solutions. This presentation explores the latest research that is enabling efficient 3D perception while maintaining neural network model accuracy. You’ll learn about:
- The advantages of 3D perception over 2D and the need for 3D perception across applications
- Advancements in 3D perception research by Qualcomm AI Research
- Our future 3D perception research directions
The 2016 Remote Sensing Field camp will take the form of two projects.
A low tech, low cost aerial photography project using visible spectrum UAV/Ultralight Aircraft mounted cameras as the sensor to demonstrate that relatively low tech, low cost solutions can achieve surprisingly good results when compared to more commercial systems.
A more high tech, high cost terrestrial LiDAR collect of a building or structure of historical or architectural significance.
The scope of a project will influence all other aspects of the project, including its cost, timing, quality and risk.
Application of Foundation Model for Autonomous DrivingYu Huang
Since DARPA’s Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Recently powered by large language models (LLMs), chat systems, such as chatGPT and PaLM, emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) in natural language processing (NLP). There comes a natural thinking that we could employ these abilities to reformulate autonomous driving. By combining LLM with foundation models, it is possible to utilize the human knowledge, commonsense and reasoning to rebuild autonomous driving systems from the current long-tailed AI dilemma. In this paper, we investigate the techniques of foundation models and LLMs applied for autonomous driving, categorized as simulation, world model, data annotation and planning or E2E solutions etc.
Fisheye based Perception for Autonomous Driving VIYu Huang
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images
Fisheye/Omnidirectional View in Autonomous Driving VYu Huang
Road-line detection and 3D reconstruction using fisheye cameras
• Vehicle Re-ID for Surround-view Camera System
• SynDistNet: Self-Supervised Monocular Fisheye Camera Distance
Estimation Synergized with Semantic Segmentation for Autonomous
Driving
• Universal Semantic Segmentation for Fisheye Urban Driving Images
• UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a
Generic Framework for Handling Common Camera Distortion Models
• OmniDet: Surround View Cameras based Multi-task Visual Perception
Network for Autonomous Driving
• Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
Fisheye/Omnidirectional View in Autonomous Driving IVYu Huang
FisheyeMultiNet: Real-time Multi-task Learning Architecture for
Surround-view Automated Parking System
• Generalized Object Detection on Fisheye Cameras for Autonomous
Driving: Dataset, Representations and Baseline
• SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
Autonomous Driving
• Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors
for the ADAS
Autonomous driving for robotaxi, like perception, prediction, planning, decision making and control etc. As well as simulation, visualization and data closed loop etc.
LiDAR in the Adverse Weather: Dust, Snow, Rain and Fog (2)Yu Huang
Canadian Adverse Driving Conditions Dataset, 2020, 2
Deep multimodal sensor fusion in unseen adverse weather, 2020, 8
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather, 2021, 4
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection, 2021, 7
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather, 2021, 8
DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather, 2021, 9
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World, 2020
A Scenario-Based Development Framework for Autonomous Driving, 2020
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving, 2020
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction, 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles, 2021
Systems Approach to Creating Test Scenarios for Automated Driving Systems, Reliability Engineering and System Safety (215), 2021
How to Build a Data Closed-loop Platform for Autonomous Driving?Yu Huang
Introduction;
data driven models for autonomous driving;
cloud computing infrastructure and big data processing;
annotation tools for training data;
large scale model training platform;
model testing and verification;
related machine learning techniques;
Conclusion.
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
Prediction and planning for self driving at waymoYu Huang
ChauffeurNet: Learning To Drive By Imitating The Best Synthesizing The Worst
Multipath: Multiple Probabilistic Anchor Trajectory Hypotheses For Behavior Prediction
VectorNet: Encoding HD Maps And Agent Dynamics From Vectorized Representation
TNT: Target-driven Trajectory Prediction
Large Scale Interactive Motion Forecasting For Autonomous Driving : The Waymo Open Motion Dataset
Identifying Driver Interactions Via Conditional Behavior Prediction
Peeking Into The Future: Predicting Future Person Activities And Locations In Videos
STINet: Spatio-temporal-interactive Network For Pedestrian Detection And Trajectory Prediction
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
fusion of Camera and lidar for autonomous driving I
1. Fusion of Camera and LiDAR for
Autonomous Vehicles I
(via Deep Learning)
Yu Huang
Yu.huang07@gmail.com
Sunnyvale, California
2. Outline
• A General Pipeline for 3D Detection of Vehicles
• Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian
Detection
• Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for Deep Object
Detection
• PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
• RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
• Joint 3D Proposal Generation and Object Detection from View Aggregation
• Frustum PointNets for 3D Object Detection from RGB-D Data
• Deep Continuous Fusion for Multi-Sensor 3D Object Detection
• Multi-View 3D Object Detection Network for Autonomous Driving
• End-to-end Learning of Multi-sensor 3D Tracking by Detection
3. A General Pipeline for 3D Detection of
Vehicles
• Autonomous driving requires 3D perception of vehicles and other objects in the in
environment.
• Much of the current methods support 2D vehicle detection.
• Here is a pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to
generate 3D information with minimum changes of the 2D detection networks.
• To identify the 3D box, a model fitting algorithm is developed based on generalized car
models and score maps.
• A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box.
• It requires minimum efforts to modify the existing 2D networks to fit into the pipeline,
adding just one additional regression term at the output layer to estimate the vehicle
dimensions.
4. A General Pipeline for 3D Detection of
Vehicles
The raw image is passed to a 2D detection network which provides 2D boxes around the vehicles in
the image plane. Subsequently, a set of 3D points which fall into the 2D bounding box after projection
is selected. With this set, a model fitting algorithm detects the 3D location and 3D bounding box of
the vehicle. And then another CNN network, which takes the points that fit into the 3D bounding box
as input, carries out the final 3D box regression and classification.
5. Combining LiDAR Space Clustering and Convolutional
Neural Networks for Pedestrian Detection
• This is a pedestrian detector that exploits LiDAR data, in addition to visual information.
• The hypothesis is that using depth data and prior info about the size of the objects, it can
reduce the search space by providing candidates and, speeding up detection algorithms.
• A hypothesis is that this prior definition of the location and size of the candidate bounding
box will also decrease the number of false detections.
• In the approach, LiDAR data is utilized to generate region proposals by processing the three
dimensional point cloud that it provides.
• These candidate regions are then further processed by a state-of-the-art CNN classifier that
is fine-tuned for pedestrian detection.
6. Combining LiDAR Space Clustering and Convolutional
Neural Networks for Pedestrian Detection
The algorithm is built upon the idea of clustering the 3-D point cloud of the LiDAR. It starts
with raw measurements down-sampling, followed by removal of the floor plane. Then, a
density-based clustering algorithm generates the candidates that are projected on the image
space to provide a region of interest.
7. Fusing Bird’s Eye View LIDAR Point Cloud and Front
View Camera Image for Deep Object Detection
• This is a method for fusing LIDAR point cloud and camera-captured images in deep
convolutional neural networks (CNN).
• The method constructs a layer called sparse non-homogeneous pooling layer to transform
features between bird’s eye view and front view.
• The sparse point cloud is used to construct the mapping between the two views.
• The pooling layer allows efficient fusion of the multi-view features at any stage of the
network.
• This is favorable for 3D object detection using camera-LIDAR fusion for autonomous driving.
• A corresponding one-stage detector is designed and tested on the KITTI bird’s eye view
object detection dataset, which produces 3D bounding boxes from the bird’s eye view
map.
• The fusion method shows significant improvement on both speed and accuracy of the
pedestrian detection over other fusion-based object detection networks.
8. Fusing Bird’s Eye View LIDAR Point Cloud and Front
View Camera Image for Deep Object Detection
The sparse non-homogeneous pooling layer that fuses front view image and bird’s eye view LIDAR feature.
9. Fusing Bird’s Eye View LIDAR Point Cloud and Front
View Camera Image for Deep Object Detection
The fusion-based one-stage object detection network with MS-CNN networks.
10. PointFusion: Deep Sensor Fusion for 3D
Bounding Box Estimation
• PointFusion, a generic 3D object detection method leverages both image and 3D point
cloud information.
• Unlike existing methods that either use multi-stage pipelines or hold sensor and dataset-
specific assumptions, PointFusion is conceptually simple and application- agnostic.
• It consists of: an off-the-shelf CNN that extracts appearance and geometry features from
input RGB image crops, a variant of PointNet that processes the raw 3D point cloud, and a
fusion sub-network that combines the two outputs to predict 3D bounding boxes.
• The image data and the raw point cloud data are independently processed by a CNN and a
PointNet architecture, respectively.
• The resulting outputs are then combined by a fusion network, which predicts multiple 3D
box hypotheses and their confidences, using the input 3D points as spatial anchors.
11. PointFusion: Deep Sensor Fusion for 3D
Bounding Box Estimation
Two feature extractors: a PointNet variant that processes raw point cloud data (A), and a CNN that extracts visual
features from an input image (B). Two fusion network formulations: a vanilla global architecture that directly regresses
the box corner locations (D), and a dense architecture that predicts the spatial offset of each of the 8 corners relative to
an input point, (C): for each input point, the network predicts the spatial offset (white arrows) from a corner (red dot) to
the input point (blue), and selects the prediction with the highest score as the final prediction (E).
12. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
• RoarNet, an approach for 3D object detection from 2D image and 3D Lidar point clouds.
• Based on two stage object detection framework with PointNet as backbone network.
• The first part, RoarNet 2D, estimates the 3D poses of objects from a monocular image, which
approximates where to examine further, and derives multiple candidates that are
geometrically feasible.
• This step significantly narrows down feasible 3D regions, which otherwise requires
demanding processing of 3D point clouds in a huge search space.
• The second part, RoarNet 3D, takes the candidate regions and conducts in-depth inferences
to conclude final poses in a recursive manner.
• RoarNet 3D processes 3D point clouds without any loss of data, leading to precise detection.
13. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
The model first predicts the 2D bounding boxes and a 3D poses of objects from a 2D image. For each 2D object
detection, geometric agreement search is applied to predict the location of object in 3D space. Centered on
each location prediction, set region proposal which has a shape of standing cylinder. Taking the prediction error
in bounding box and pose into account, there can be multiple region proposals for a single object.
14. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
• Each region proposal is responsible for detecting a single object.
• Taking the point clouds sampled from each region proposal as input, the model predicts the
location of an object relative to the center of region proposal, which recursively serves for
setting new region proposals for the next step.
• The model also predicts objectness score which reflects the probability of an object being
inside the region proposal.
• Only those proposals with high objectness scores are considered at the next step.
• At a final step, the model sets new region proposals at previously predicted locations.
• The model predicts all coordinates required for 3D bounding box regression including
location, rotation, and size of the objects.
• For practical reason, repeating this step more than once gives better detection performance.
15. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
Architecture of RoarNet 2D
16. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
The backbone network is a simplified version of PointNet without T-Net.
17. RoarNet: A Robust 3D Object Detection based
on RegiOn Approximation Refinement
18. Joint 3D Proposal Generation and Object
Detection from View Aggregation
• AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
• The neural network architecture uses LIDAR point clouds and RGB images to generate
features that are shared by two subnetworks: a region proposal network (RPN) and a second
stage detector network.
• The RPN uses an architecture capable of performing multimodal feature fusion on high
resolution feature maps to generate reliable 3D object proposals for multiple object classes
in road scenes.
• Using these proposals, the second stage detection network performs accurate oriented 3D
bounding box regression and category classification to predict the extents, orientation, and
classification of objects in 3D space.
• The proposed architecture produces SoA results on the KITTI 3D object detection
benchmark while running in real time with a low memory footprint.
• Code is https://github.com/kujason/avod
19. Joint 3D Proposal Generation and Object
Detection from View Aggregation
The method’s architectural diagram. The feature extractors are shown in blue, the region proposal network in
pink, and the second stage detection network in green.
20. Joint 3D Proposal Generation and Object
Detection from View Aggregation
The architecture of proposed high resolution feature extractor
shown here for the image branch. Feature maps are
propagated from the encoder to the decoder section via red
arrows. Fusion is then performed at every stage of the decoder
by a learned upsampling layer, followed by concatenation, and
then mixing via a convolutional layer, resulting in a full
resolution feature map at the last layer of the decoder.
21. Joint 3D Proposal Generation and Object
Detection from View Aggregation
A visual comparison between the 8 corner box encoding, the
axis aligned box encoding, and our 4 corner encoding.
22. Joint 3D Proposal Generation and Object
Detection from View Aggregation
Left: 3D region proposal network output, Middle: 3D detection output, and Right: the projection of the
detection output onto image space for all three classes. The 3D LIDAR point cloud has been colorized and
interpolated for better visualization.
23. Frustum PointNets for 3D Object Detection
from RGB-D Data
• 3D object detection from RGB- D data in both indoor and outdoor scenes.
• While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns
and invariances of 3D data, here directly operate on raw point clouds by popping up RGB-D
scans.
• However, a key challenge of this approach is how to efficiently localize objects in point
clouds of large-scale scenes (region proposal).
• Instead of solely relying on 3D proposals, this method leverages both mature 2D object
detectors and advanced 3D deep learning for object localization, achieving efficiency as well
as high recall for even small objects.
• Benefited from learning directly in raw point clouds, this method is also able to precisely
estimate 3D bounding boxes even under strong occlusion or with very sparse points.
• Evaluated on KITTI and SUN RGB-D 3D detection benchmarks.
24. Frustum PointNets for 3D Object Detection
from RGB-D Data
3D object detection pipeline. Given RGB-D data, first generate 2D object region proposals in the RGB
image using a CNN. Each 2D region is then extruded to a 3D viewing frustum in which to get a point
cloud from depth data. Finally, the frustum PointNet predicts a (oriented and amodal) 3D bounding box
for the object from the points in frustum.
25. Frustum PointNets for 3D Object Detection
from RGB-D Data
Frustum PointNets for 3D object detection. First leverage a 2D CNN object detector to propose 2D regions and
classify their content. 2D regions are then lifted to 3D and thus become frustum proposals. Given a point cloud
in a frustum (n × c with n points and c channels of XYZ, intensity etc. for each point), the object instance is
segmented by binary classification of each point. Based on the segmented object point cloud (m × c), a light-
weight regression PointNet (T-Net) tries to align points by translation such that their centroid is close to amodal
box center. At last the box estimation net estimates the amodal 3D bounding box for the object.
26. Frustum PointNets for 3D Object Detection
from RGB-D Data
Coordinate systems for point cloud. Artificial points (black dots) are shown to
illustrate (a) default camera coordinate; (b) frustum coordinate after rotating
frustums to center view; (c) mask coordinate with object points’ centroid at
origin; (d) object coordinate predicted by T-Net.
27. Frustum PointNets for 3D Object Detection
from RGB-D Data
Basic architectures and IO for PointNets. Architecture is illustrated for PointNet++ (v2)
models with set abstraction layers and feature propagation layers (for segmentation).
28. Frustum PointNets for 3D Object Detection
from RGB-D Data
True positive detection boxes are in green, while false positive boxes are in red and ground truth
boxes in blue are shown for false positive and false negative cases. Digit and letter beside each box
denote instance id and semantic class, with “v” for cars, “p” for pedestrian and “c” for cyclist.
29. Frustum PointNets for 3D Object Detection
from RGB-D Data
Network architectures for Frustum PointNets. v1 models are based on PointNet. v2 models are based on PointNet++ set
abstraction (SA) and feature propagation (FP) layers. The architecture for residual center estimation T-Net is shared for v1
and v2. The colors (blue for segmentation nets, red for T-Net and green for box estimation nets) of the network background
indicate the coordinate system of the input point cloud. Segmentation nets operate in frustum coordinate, T-Net processes
points in mask coordinate while box estimation nets take points in object coordinate. The small yellow square (or bar)
concatenated with global features is class one-hot vector that tells the predicted category of the underlying object.
30. Deep Continuous Fusion for Multi-Sensor
3D Object Detection
• It remains an open problem to design 3D detectors that can better exploit multiple
modalities.
• A 3D object detector can exploit both LIDAR as well as cameras to perform very accurate
localization.
• It reasons in bird’s eye view (BEV) and fuses image features by learning to project them
into BEV space.
• Towards this goal, an end-to-end learnable architecture exploits continuous convolutions to
fuse image and LIDAR feature maps at different levels of resolution.
• The proposed continuous fusion layer encode both discrete-state image features as well as
continuous geometric information.
• This enables designing a reliable and efficient end-to-end learnable 3D object detector
based on multiple sensors.
31. Deep Continuous Fusion for Multi-Sensor
3D Object Detection
Architecture of model. There are two streams, namely the camera image stream and the BEV LIDAR stream.
Continuous fusion layers are used to fuse the image features onto the BEV feature maps.
32. Deep Continuous Fusion for Multi-Sensor
3D Object Detection
Continuous fusion layer: given a target pixel on BEV image, first extract K nearest LIDAR points; then project the 3D
points onto the camera image plane; this helps retrieve corresponding image features; finally feed the image
feature + continuous geometry offset into a MLP to generate feature for the target pixel.
33. Deep Continuous Fusion for Multi-Sensor
3D Object Detection
The 2D bounding boxes are obtained by projecting the 3D detections onto the image.
The bounding box of an object on BEV and images are shown in the same color.
34. Multi-View 3D Object Detection Network
for Autonomous Driving
• Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point
cloud and RGB images as input and predicts oriented 3D bounding boxes.
• It encodes the sparse 3D point cloud with a compact multi-view representation.
• The network is composed of two subnetworks: one for 3D object proposal generation and
another for multi-view feature fusion.
• The proposal network generates 3D candidate boxes efficiently from the bird’s eye view
representation of 3D point cloud.
• A deep fusion scheme combines region-wise features from multiple views and enables
interactions between intermediate layers of different paths.
35. Multi-View 3D Object Detection Network
for Autonomous Driving
Input features of the MV3D network.
36. Multi-View 3D Object Detection Network
for Autonomous Driving
The network takes the bird’s eye view and front view of LIDAR point cloud as well as an image as input. It first
generates 3D object proposals from bird’s eye view map and project them to three views. A deep fusion
network is used to combine region-wise features obtained via ROI pooling for each view. The fused features
are used to jointly predict object class and do oriented 3D box regression.
37. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
• This task, commonly referred to as Multi-target tracking, consists on identifying how many
objects there are in each frame, as well as link their trajectories over time.
• It is an approach to tracking by detection that can exploit both cameras as well as LIDAR
data to produce very accurate 3D trajectories.
• Towards this goal, it formulates the problem as inference in a deep structured model, where
the potentials are computed using convolutional neural nets.
• The matching cost of associating two detections exploits both appearance and motion via a
Siamese network that processes images and motion representations via convolutional layers.
• Inference in the model can be done exactly and efficiently by a set of feedforward passes
followed by solving a linear program.
• Importantly, the model is formulated such that it can be trained end-to-end to solve both
the detection and tracking problems.
38. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
In this work, it formulates tracking as a system containing multiple neural networks that are interwoven
together in a single architecture. Note that the system takes as external input a time series of RGB Frames
(camera images) and LIDAR point clouds. From these inputs, the system produces discrete trajectories of the
targets. In particular, an architecture is end to end trainable while still maintaining explainability, which is
achieved by formulating the system in a structured manner.
39. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
Neural networks designed for both
scoring and matching: the forward passes
over a set of detections from two frames.
40. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
• To extract appearance features, employ a Siamese network based on VGG16.
• Note that in a Siamese setup, the two branches (each processing a detection) share the
same set of weights.
• This makes the architecture more efficient in terms of memory and allows learning with
fewer examples.
• In particular, resize each detection to be of dimension 224 × 224.
• To produce a concise representation of activations without using fully connected layers, each
of the max-pool outputs is passed through a product layer followed by a weighted sum,
which produces a single scalar for each max-pool layer, yielding an activation vector size 5.
• Use skip-pooling as matching should exploit both low-level features (e.g., color) as well as
semantically richer features from higher layers.
• To incorporate spatial information into the model, employ fully connected architectures that
model both 2D and 3D motion.
41. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
• In particular, exploit 3D information in the form of a 180 × 200 occupancy grid in bird’s
eye view and 2D information from the occupancy region in the frontal view camera, scaled
down from the original resolution of 1242 × 375 to 124 × 37.
• In bird’s eye perspective, each 3D detection is projected onto a ground plane, leaving only
a rotated rectangle that reflects its occupancy in the world.
• Since the observer is a mobile platform (an autonomous vehicle, in this case), the coordinate
system between two subsequent frames would be shifted because the observer moved in
the time elapsed.
• Since its speed in each axis is known from the IMU data, one can calculate the displacement
of the observer between each observation and translate the coordinates accordingly; this
way, both grids are on the exact same coordinate system.
• The frontal view perspective encodes the rectangular area in the camera occupied by the
target, equivalent of projecting the 3D bounding box onto camera coordinates.
43. End-to-end Learning of Multi-sensor 3D
Tracking by Detection
Trajectories are color coded, such that having the same color means it’s the same object.