A PyTorch Library for Accelerating 3D Deep Learning Research
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Updated
May 16, 2024 - Python
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.
3DMatch - a 3D ConvNet-based local geometric descriptor for aligning 3D meshes and point clouds.
[ECCV'20] Convolutional Occupancy Networks
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.
KITTI data processing and 3D CNN for Vehicle Detection
3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.
Research platform for 3D object detection in PyTorch.
[CVPR'23] Learning Neural Parametric Head Models
[Siggraph '23] NeRSemble: Neural Radiance Field Reconstruction of Human Heads
[ICCVW-2021] SA-Det3D: Self-attention based Context-Aware 3D Object Detection
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