3d-deep-learning

A PyTorch Library for Accelerating 3D Deep Learning Research

🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey

NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).

pyntcloud is a Python library for working with 3D point clouds.

Python code to fuse multiple RGB-D images into a TSDF voxel volume.

[ECCV'20] Convolutional Occupancy Networks

3DMatch - a 3D ConvNet-based local geometric descriptor for aligning 3D meshes and point clouds.

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

Fuse multiple depth frames into a TSDF voxel volume.

This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.

Pytorch code to construct a 3D point cloud model from single RGB image.

3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.

KITTI data processing and 3D CNN for Vehicle Detection

[CVPR'23] Learning Neural Parametric Head Models

[Siggraph '23] NeRSemble: Neural Radiance Field Reconstruction of Human Heads

Research platform for 3D object detection in PyTorch.

[ICCVW-2021] SA-Det3D: Self-attention based Context-Aware 3D Object Detection

[AAAI'20] LCD: Learned Cross-Domain Descriptors for 2D-3D Matching