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/konyul/FG-PFE
공식 코드Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
https://github.com/kuanchihhuang/moma-m3t
공식 코드Delving into Motion-Aware Matching for Monocular 3D Object Tracking
https://github.com/kuanchihhuang/monodtr
공식 코드MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
https://github.com/kuanchihhuang/ptt
공식 코드PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection
https://github.com/kuanchihhuang/vg-w3d
공식 코드Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance
https://github.com/kujason/avod
공식 코드Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/lanlan96/3drm
공식 코드https://github.com/lanlan96/arm3d
공식 코드ARM3D: Attention-based relation module for indoor 3D object detection
https://github.com/laughtervv/SGPN
공식 코드SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
https://github.com/ldkong1205/Robo3D
공식 코드Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
https://github.com/ldtho/pifenet
공식 코드Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network
https://github.com/leofansq/QUEST
공식 코드https://github.com/lianqing11/monojsg
공식 코드MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
https://github.com/lidar-perception/lidar-cs
공식 코드LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
https://github.com/liewfeng/beam
공식 코드Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
https://github.com/liewfeng/raydn
공식 코드Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
https://github.com/likojack/odam
공식 코드ODAM: Object Detection, Association, and Mapping using Posed RGB Video
https://github.com/linkon87/exploring-diversity-based-active-learning-for-3d-object-detection-in-autonomous-driving
공식 코드Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
https://github.com/linxuewu/sparse4d
공식 코드Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion
https://github.com/littlepey/ptpm
공식 코드Semi-supervised 3D Object Detection with PatchTeacher and PillarMix
https://github.com/liyih/corenet
공식 코드https://github.com/liyih/rctrans
공식 코드RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
https://github.com/liz66666/gpa3d
공식 코드https://github.com/lizhuoling/voxelformer-public
공식 코드https://github.com/luluho1208/diffusion-ss3d
공식 코드Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
https://github.com/luoyadan/crb-active-3ddet
공식 코드Exploring Active 3D Object Detection from a Generalization Perspective
https://github.com/lyhdet/ov-3det
공식 코드Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
https://github.com/lzccccc/SMOKE
공식 코드SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
https://github.com/m-g-a/3d-metrics
공식 코드Bounding Box Disparity: 3D Metrics for Object Detection With Full Degree of Freedom
https://github.com/m-g-a/care3d
공식 코드Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments
https://github.com/malicd/sailor
공식 코드SAILOR: Scaling Anchors via Insights into Latent Object Representation
https://github.com/manymuch/ground_normal_filter
공식 코드Towards Accurate Ground Plane Normal Estimation from Ego-Motion
https://github.com/maxiuw/uada3d
공식 코드https://github.com/mayechi/lt3d-lf
공식 코드https://github.com/mcg-nju/link
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.