Foss4G Japan 2021 シェープファイルの真の後継規格 FlatGeobufの普及啓蒙活動Kanahiro Iguchi
This document summarizes a presentation about FlatGeobuf, a new vector format standard that aims to replace Shapefiles. It introduces FlatGeobuf as a simpler format than GeoPackage that addresses Shapefile limitations like file size restrictions and character encoding issues. FlatGeobuf uses FlatBuffers and stores geometry and attributes in a single file, offering high performance for reading, writing, and streaming partial data compared to other formats. The document advocates for adopting FlatGeobuf as the new standard vector format.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
[DL輪読会]PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object DetectionDeep Learning JP
This paper proposes a new method called PV-RCNN for 3D object detection from point clouds. It introduces two key modules: 1) A voxel-to-keypoint scene encoding module that extracts feature vectors for keypoints by combining features from voxel CNNs and point networks. 2) A RoI grid pooling module that computes feature vectors for regions of interest (RoIs) from the keypoint features to refine detections. Experiments on KITTI and Waymo datasets demonstrate that PV-RCNN achieves state-of-the-art performance for 3D object detection from point clouds.
Foss4G Japan 2021 シェープファイルの真の後継規格 FlatGeobufの普及啓蒙活動Kanahiro Iguchi
This document summarizes a presentation about FlatGeobuf, a new vector format standard that aims to replace Shapefiles. It introduces FlatGeobuf as a simpler format than GeoPackage that addresses Shapefile limitations like file size restrictions and character encoding issues. FlatGeobuf uses FlatBuffers and stores geometry and attributes in a single file, offering high performance for reading, writing, and streaming partial data compared to other formats. The document advocates for adopting FlatGeobuf as the new standard vector format.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
[DL輪読会]PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object DetectionDeep Learning JP
This paper proposes a new method called PV-RCNN for 3D object detection from point clouds. It introduces two key modules: 1) A voxel-to-keypoint scene encoding module that extracts feature vectors for keypoints by combining features from voxel CNNs and point networks. 2) A RoI grid pooling module that computes feature vectors for regions of interest (RoIs) from the keypoint features to refine detections. Experiments on KITTI and Waymo datasets demonstrate that PV-RCNN achieves state-of-the-art performance for 3D object detection from point clouds.