Lingdong Kong's Projects
Papers and Datasets about Point Cloud.
A curated list of awesome 3D domain adaptation resources
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
[ICRA 2023] ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
A general 3D object detection codebse.
Detectron2 is FAIR's next-generation platform for object detection and segmentation.
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
[CVPR 2023 Highlight] LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving
Markdown εΊζ¬θ―ζ³γ
[TNNLS'21] Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks
OpenMMLab's next-generation platform for general 3D object detection.
Graduate-Level Courses (Electrical Engineering & Computer Science) at NTU, Singapore
[ROBIOβ18] Comparisons among Six Numerical Methods for Solving Repetitive Motion Planning of Redundant Robot Manipulators
An extension of Open3D to address 3D Machine Learning tasks
[CVPR 2024 Highlight] OpenESS: Event-Based Semantic Scene Understanding with Open Vocabularies
Point Density-Aware Voxels for LiDAR 3D Object Detection (CVPR 2022)
A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks [ECCV 2020]
Benchmarking and Analyzing Point Cloud Perception Robustness under Corruptions
Pytorch implementation of convolutional neural network visualization techniques
Deep Learning (with PyTorch)
[ICCV 2023] Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
RoboBEV: Robust Bird's Eye View Detection under Corruptions
[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation