https://github.com/zyrant/cpdet3d
공식 코드Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection
Papers with Code 아카이브
3D Object Detection
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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/zyrant/spgroup3d
공식 코드SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection
https://github.com/zyyyyy0926/pointcformer_plus_pytorch
공식 코드PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion
https://github.com/zzj403/BEV_Robust
공식 코드https://github.com/zzy816/com
공식 코드Curricular Object Manipulation in LiDAR-based Object Detection
https://gitlab.au.dk/maleci/opendr/ua-ab3dmot
공식 코드Uncertainty-Aware AB3DMOT by Variational 3D Object Detection
https://gitlab.au.dk/maleci/opendr/variational-voxel-3d-detetction
공식 코드Uncertainty-Aware AB3DMOT by Variational 3D Object Detection
https://gitee.com/gai-shaoyan/mind3d
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/15526837635/CenterPoint
https://github.com/2023-MindSpore-1/ms-code-179
Bridged Transformer for Vision and Point Cloud 3D Object Detection
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/frustum-pointnet
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/TokenFusion
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/SRC
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/textrcnn
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/pointRCNN
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/2023-MindSpore-1/ms-code-39
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CascadeRCNN
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TokenFusion
https://github.com/2023-MindSpore-4/Code15/tree/main/wangyikai/TokenFusion
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet2
https://github.com/2024-MindSpore-1/Code2/tree/main/wangyikai/EIP-mindspore
https://github.com/2gunsu/monocon-pytorch
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection
https://github.com/3epochs/you-only-look-once
https://github.com/497626895/darknet
https://github.com/593903762/center
https://github.com/97andrea97/CenterNet-PNPDet
https://github.com/AGroupofProbiotocs/YOLO_V2_in_Tensorflow
https://github.com/AI-liu/Complex-YOLO
https://github.com/AlexeyAB/darknet
https://github.com/AllenPeng0209/deeproute_competition
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/AlongRide/CenterNet_anchor_free
https://github.com/AlphaGoMK/arxiv-explorer
3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/AmrElsersy/PointPainting
https://github.com/ArashJavan/PointSeg
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.