There are no reviews yet. Be the first to send feedback to the community and the maintainers!
WoodScape
The repository containing tools and information about the WoodScape dataset.ADVENT
Adversarial Entropy Minimization for Domain Adaptation in Semantic SegmentationLOST
Pytorch implementation of LOST unsupervised object discovery methodxmuda
Cross-Modal Unsupervised Domain Adaptationfor 3D Semantic SegmentationZS3
Zero-Shot Semantic SegmentationPOCO
SLidR
Official PyTorch implementation of "Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data"ALSO
ALSO: Automotive Lidar Self-supervision by Occupancy estimationConfidNet
Addressing Failure Prediction by Learning Model ConfidenceRADIal
Maskgit-pytorch
BF3S
Boosting Few-Shot Visual Learning with Self-SupervisionDADA
Depth-aware Domain Adaptation in Semantic SegmentationFLOT
FLOT: Scene Flow Estimation by Learned Optimal Transport on Point Cloudsobow
carrada_dataset
rangevit
PointBeV
Official implementation of PointBeV: A Sparse Approach to BeV Predictionsrainbow-iqn-apex
Distributed Rainbow-IQN for AtariBEVContrast
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds - Official PyTorch implementationFOUND
PyTorch code for Unsupervised Object Localization: Observing the Background to Discover ObjectsAwesome-Unsupervised-Object-Localization
Curated list of awesome works on unsupervised object localization in 2D images.LightConvPoint
MVRSS
WaffleIron
LaRa
LaRa: Latents and Rays for Multi-Camera Bird’s-Eye-View Semantic SegmentationSALUDA
Public repository for the 3DV 2024 spotlight paper "SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation".ScaLR
PyTorch code and models for ScaLR image-to-lidar distillation method3DGenZ
Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"obsnet
BUDA
Boundless Unsupervised Domain Adaptation in Semantic SegmentationSemanticPalette
Semantic Palette: Guiding Scene Generation with Class Proportionsxmuda_journal
[TPAMI] Cross-modal Learning for Domain Adaptation in 3D Semantic SegmentationGenVal
Reliability in Semantic Segmentation: Can We Use Synthetic Data? (ECCV 2024)PCAM
NeeDrop
NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle DroppingMTAF
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic SegmentationESL
ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic SegmentationSTEEX
STEEX: Steering Counterfactual Explanations with SemanticsTTYD
Public repository for the ECCV 2024 paper "Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation".OCTET
CAB
Occfeat
MuHDi
Official PyTorch implementation of "Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation"diffhpe
Official code of "DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion"bravo_challenge
BRAVO Challenge Toolkit and Evaluation Codesfrik
Official code for "Self-supervised learning with rotation-invariant kernels"BEEF
MFEval
[ICRA2024] Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive? This is the official implementation of the evaluation protocol proposed in this work for motion forecasting models with real-world perception inputs.MOCA
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook AssignmentsSP4ASC
bownet
Learning Representations by Predicting Bags of Visual WordsQuEST
UNIT
UNIT: Unsupervised Online Instance Segmentation through Time - Official PyTorch implementationPAFUSE
Official repository of PAFUSEdl_utils
The library used in the Valeo Deep learning training.tutorial-images
valeoai.github.io
MF_aWTA
This is official implementation for annealed Winner-Takes-All loss in <Annealed Winner-Takes-All for Motion Forecasting>.Love Open Source and this site? Check out how you can help us