Stars
Soruce code of OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
PccAI is a framework for AI-based point cloud compression
[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.
Source code for the paper "TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations"
Source code of "GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression"
A pytorch implementation of Deep Graph Laplacian Regularization for image denoising
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Papers and Datasets about Point Cloud.
Source code for the paper "FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds"
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
A PyTorch library and evaluation platform for end-to-end compression research
Improved Deep Point Cloud Geometry Compression
Folding-based compression of point cloud attributes
PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows
A clean PointNet++ segmentation model implementation. Support batch of samples with different number of points.
Graph Neural Network Library for PyTorch
A list of papers and datasets about point cloud analysis (processing)
WebGL point cloud viewer for large datasets
Off-the-shelf deep alignment and refinement for weakly calibrated ToF RGB-D modules
Experiments with Deep Learning
955 不加班的公司名单 - 工作 955，work–life balance (工作与生活的平衡)
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Demo code of the paper: "Learning to Segment Instances in Videos with Spatial Propagation Network", in CVPR'17 Workshop on DAVIS Challenge