Stars
Using mmWave radars for some computer vision tasks.
This is the repository of mmEMP, which has been published on ICRA 2024: Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision.
Python (PyTorch) code and model of "Point Cloud Denoising via Momentum Ascent in Gradient Fields"
Code repository for paper: Fast and Scalable Human Pose Estimation using mmWave Point Cloud
The official implementation of 4DenoiseNet
Learning Graph-Convolutional Representations for Point Cloud Denoising (ECCV 2020)
Official code for the paper: Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
[ACM MM 2024] Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [PointNeXt](https://arxiv.org/abs/2206.04670)
[NeurIPS 2023] PointGPT: Auto-regressively Generative Pre-training from Point Clouds
It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs
[ICLR 2022 poster] Official PyTorch implementation of "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework"
Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar
Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar
Radar-based Interior Object Classification Dataset
3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.
Pointcept: Perceive the world with sparse points, a codebase for point cloud perception research. Latest works: Concerto (NeurIPS'25), Sonata (CVPR'25 Highlight), PTv3 (CVPR'24 Oral)