Stars
CUDA integration for Python, plus shiny features
Fast Corrects for fisheye distortion in an image.
A comprehensive list of awesome contrastive self-supervised learning papers.
[AAAI 2024] BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Code for the paper "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds" (ICCV 2021)
Code for the paper "Masked Autoencoders for Self-Supervised Learning on Automotive Point Clouds"
Official implementation of our TIV'23 paper: Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds - Official PyTorch implementation
fusion_pointclouds 主要目的为Ubuntu环境下无人车多激光雷达标定之后, 将多个激光雷达点云话题/坐标系 通过PCL (Point Cloud Library)融合为 一个ros点云话题,以便于后期点云地面分割与地面处理等等。
The code for calibration between lidars. (Chinese Version)
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
[CVPR'24 Oral] Official repository of Point Transformer V3 (PTv3)
《代码随想录》LeetCode 刷题攻略:200道经典题目刷题顺序,共60w字的详细图解,视频难点剖析,50余张思维导图,支持C++,Java,Python,Go,JavaScript等多语言版本,从此算法学习不再迷茫!🔥🔥 来看看,你会发现相见恨晚!🚀
Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
deep learning for image processing including classification and object-detection etc.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
A Simple PointPillars PyTorch Implementation for 3D LiDAR(KITTI) Detection.
Convert pointpillars Pytorch Model To ONNX for TensorRT Inference
A project demonstrating how to use CUDA-PointPillars to deal with cloud points data from lidar.
StructToken : Rethinking Semantic Segmentation with Structural Prior
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
PointNet ++ implementation on PyTorch for semantic segmentation of point clouds