Popular GitHub Repositories for Ld19 Lidar

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

An extension of Open3D to address 3D Machine Learning tasks

Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

[IEEE T-PAMI 2023] Awesome BEV perception research and cookbook for all level audience in autonomous diriving

Generate Subtitles & Diarize Speakers in Davinci Resolve using AI.

A collection of resources on applications of Transformers in Medical Imaging.

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.

Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

Tensorflow implementation of the SRGAN algorithm for single image super-resolution

Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).

C++ implementation to Detect, track and classify multiple objects using LIDAR scans or point cloud

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023)

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

A list of references on lidar point cloud processing for autonomous driving

ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"