Two sentences are tokenized and encoded by a BERT model. The first sentence describes two kids playing with a green crocodile float in a swimming pool. The second sentence describes two kids pushing an inflatable crocodile around in a pool. The tokenized sentences are passed through the BERT model, which outputs the encoded representations of the token sequences.
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
Two sentences are tokenized and encoded by a BERT model. The first sentence describes two kids playing with a green crocodile float in a swimming pool. The second sentence describes two kids pushing an inflatable crocodile around in a pool. The tokenized sentences are passed through the BERT model, which outputs the encoded representations of the token sequences.
【DL輪読会】NeRF-VAE: A Geometry Aware 3D Scene Generative ModelDeep Learning JP
NeRF-VAE is a 3D scene generative model that combines Neural Radiance Fields (NeRF) and Generative Query Networks (GQN) with a variational autoencoder (VAE). It uses a NeRF decoder to generate novel views conditioned on a latent code. An encoder extracts latent codes from input views. During training, it maximizes the evidence lower bound to learn the latent space of scenes and allow for novel view synthesis. NeRF-VAE aims to generate photorealistic novel views of scenes by leveraging NeRF's view synthesis abilities within a generative model framework.
A x86-optimized rank&select dictionary for bit sequencesTakeshi Yamamuro
The document summarizes a technique for efficiently performing rank and select operations on bit sequences using succinct data structures. It describes splitting the bit sequence into blocks of logarithmic size and precomputing total count values (stored in arrays L and S) to allow rank and select to be performed in O(log N) time using only o(N) extra space, where N is the length of the bit sequence. This is done using a technique known as the "4 Russian methods". Performance test results show the optimized implementation outperforms existing libraries.
This document provides an overview of stereo vision algorithms and applications. It begins with an introduction to stereo vision and the correspondence problem. Key steps in a stereo vision system are discussed, including calibration, rectification, stereo matching algorithms, and triangulation. Both local and global stereo matching approaches are described. Several challenges in stereo correspondence are highlighted. The document also outlines datasets, architectures, and commercial stereo cameras for evaluation and implementation.
Comparison between Blur Transfer and Blur Re-Generation in Depth Image Based ...Norishige Fukushima
The document compares three methods for handling blur during depth image based rendering (DIBR): blur erasing, blur regeneration, and blur transfer. It proposes an improved blur transfer method that generates a mask using Canny filtering and smooths the masked region with Gaussian filtering. Experimental results show that the proposed method achieves similar subjective quality as blur regeneration with a 5x speed improvement and has the second highest PSNR scores on average. The proposed method improves blur treatment at object boundaries with only a minor computational cost increase over basic DIBR methods.
Non-essentiality of Correlation between Image and Depth Map in Free Viewpoin...Norishige Fukushima
This document summarizes an experiment on the correlation between images and depth maps in free viewpoint image coding. The experiment found that when using an accurate depth map, there is no need to consider correlation between the image and depth map. Various image codecs and post-filtering techniques were tested, and the best results were achieved using a post-filter set without a joint filter. Future work could optimize bit allocation between coded images and depth maps.