https://github.com/LittlePey/SFD
공식 코드Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Papers with Code 아카이브
3D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 67
- 보존된 평가 기록
- 806
3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: AVOD )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Liz66666/SIENet
공식 코드SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud
https://github.com/MECLabTUDA/VoxelSceneGraph
공식 코드https://github.com/MartinHahner/LiDAR_fog_sim
공식 코드Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
https://github.com/MasterHow/CoBEV
공식 코드CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
https://github.com/MikeS96/3d_obj_detection
공식 코드High-level camera-LiDAR fusion for 3D object detection with machine learning
https://github.com/NUAAXQ/MLCVNet
공식 코드MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
https://github.com/NVlabs/BundleSDF
공식 코드BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
https://github.com/NVlabs/FocalFormer3D
공식 코드FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
https://github.com/NVlabs/FoundationStereo
공식 코드FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
https://github.com/Na-Z/sess
공식 코드https://github.com/Nightmare-n/UniPAD
공식 코드UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
https://github.com/OpenDriveLab/DetAny3D
공식 코드https://github.com/OpenGVLab/PonderV2
공식 코드PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
https://github.com/OpenSpaceAI/Nesie
공식 코드Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection
https://github.com/Owen-Liuyuxuan/visualDet3D
공식 코드Ground-aware Monocular 3D Object Detection for Autonomous Driving
https://github.com/Ozzyz/carla-data-export
공식 코드https://github.com/PaddlePaddle/Paddle3D
공식 코드CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
https://github.com/QingyongHu/SoTA-Point-Cloud
공식 코드https://github.com/Robert-Mar/VFA
공식 코드https://github.com/Rock-100/MonoDet
공식 코드Geometry-based Distance Decomposition for Monocular 3D Object Detection
https://github.com/Ruiyang-061X/UA3D
공식 코드Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection
https://github.com/Sekunde/3D-SIS
공식 코드https://github.com/StiphyJay/FastPillars
공식 코드https://github.com/TRAILab/CaDDN
공식 코드Categorical Depth Distribution Network for Monocular 3D Object Detection
https://github.com/TUMFTM/RadarVoxelFusionNet
공식 코드https://github.com/Traffic-X/MonoLSS
공식 코드MonoLSS: Learnable Sample Selection For Monocular 3D Detection
https://github.com/UVA-Computer-Vision-Lab/ovmono3d
공식 코드https://github.com/VDIGPKU/OpenAD
공식 코드OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
https://github.com/Vegeta2020/CIA-SSD
공식 코드CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
https://github.com/Vegeta2020/SE-SSD
공식 코드SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
https://github.com/Vladimir2506/Pointformer
공식 코드https://github.com/WXinlong/ForeSeE
공식 코드Task-Aware Monocular Depth Estimation for 3D Object Detection
https://github.com/WYFDUT/MonoASRH
공식 코드Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
https://github.com/WangYueFt/pillar-od
공식 코드https://github.com/WeijingShi/Point-GNN
공식 코드Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.