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/Hansxsourse/Cross-Modality-Knowledge-Distillation-for-Multi-modal-Aerial-View-Object-Classification
https://github.com/Harut0726/my-pointnet-tensorflow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Harut0726/votenet
https://github.com/Heedeok/frustum-pointnets-rgbd
https://github.com/HengWeiBin/CenterFusionDetect3D
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
https://github.com/HongSic/DarknetAI
https://github.com/Hongbin98/MonoTTA
Fully Test-Time Adaptation for Monocular 3D Object Detection
https://github.com/Hub-Tian/CADNet
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
https://github.com/HungryCookie/VisDrone_CenterNet
https://github.com/IPLI/IPLI_Server
https://github.com/JakobGroeftehauge/BachelorProject
https://github.com/JakobGroeftehauge/Brick-Pose-Estimation
https://github.com/Jamie725/Multimodal-Object-Detection-via-Probabilistic-Ensembling
https://github.com/Jasonkks/mlcnet
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
https://github.com/JitindraFartiyal/Object-Detection
https://github.com/JuliaChae/M3D-RPN-Waymo
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/JunaidCS032/MOTBeyondPixels
Accurate Single Stage Detector Using Recurrent Rolling Convolution
https://github.com/JungminChung/Korean_Struc_Det
https://github.com/KaidongLi/tf-3d-alpha
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/KangchengLiu/RM3D
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/Khalifa-2020/centernet1
https://github.com/Khalifa-2020/centernet2
https://github.com/KhusDM/PointNetTree
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/KingBoyBIT/yolov3test
https://github.com/KiranAkadas/My_Pointnet_v2
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/KiritoGH/frustum-pointnets
https://github.com/KookHoiKim/GroupFree3dBaseline
https://github.com/L-Reichardt/Cylinder3D-updated-CUDA
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
https://github.com/LIU1514/Yolov3-
https://github.com/LKLQQ/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/LONG-9621/Extract_Point_3D
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LONG-9621/PointNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LONG-9621/PointNet-
https://github.com/LONG-9621/VoteNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.