Stars
A comprehensive survey of forging vision foundation models for autonomous driving, including challenges, methodologies, and opportunities.
PyCIL: A Python Toolbox for Class-Incremental Learning
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models (ICRA 2024)
[AAAI 2024] NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario.
Semantic Segmentation of Images and Point Clouds for Traversability Estimation
(ICCV 2023) Parametric Classification for Generalized Category Discovery: A Baseline Study
Source code for CVPR 2020 paper "Scene-Adaptive Video Frame Interpolation via Meta-Learning"
The demo code for the MLDG paper "Learning to Generalize: Meta-Learning for Domain Generalization", https://arxiv.org/abs/1710.03463, https://arxiv.org/pdf/1710.03463.pdf
A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
(ECCV 2022) DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation
Domain adaptation for LIDAR resource list
DomainBed is a suite to test domain generalization algorithms
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
Official implementation of "Learning to Generate Realistic LiDAR Point Clouds" (ECCV 2022)
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions (RAL 2022)
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)
Convert waymo open dataset 3D segmentation format to SemanticKITTI format.
Pytorch Implementation -- All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation, CVPR 2019
[CVPR 2022 Oral] SoftGroup for Instance Segmentation on 3D Point Clouds
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection