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深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06
[BMVC 2024] PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB Images
[MICCAI workshop 2023] Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
Multiview extension of the 3D-VAE-GAN architecture introduced in the paper "Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling"
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
[GRAIL @ MICCAI 2023] Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
[CVPR'19] Dataset and code used in the research project Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
A resource repository for 3D machine learning
Two Python scripts for extracting hand crafted features from 3D meshes and point clouds for Machine Learning. Features have been used as data for Random Forests, AdaBoost, Descision Trees, etc.
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
A curated list of awesome 3d generation papers
The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Denoising Diffusion Probabilistic Models
Official implementation of SALAD (ICCV 2023).
PartNet Dataset Official Release Repo
A new shapenet rendering 2D image dataset that also contains deph map, normal map and albedo map.
Single/multi view image(s) to voxel reconstruction using a recurrent neural network
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
This repository contains code and dataset splits for the paper "Classification by Attention: Scene Graph Classification with Prior Knowledge"
[ECCV 2018] Official code for "Graph R-CNN for Scene Graph Generation"