Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Sentient07/HeadHunter--T
공식 코드https://github.com/ShunChengWu/3DSSG
공식 코드SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
https://github.com/ShunChengWu/SceneGraphFusion
공식 코드SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
https://github.com/SilongYong/SQA3D
공식 코드https://github.com/Sina-Baharlou/Depth-VRD
공식 코드https://github.com/Sixkplus/TapLab
공식 코드TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge
https://github.com/Storife/RANet
공식 코드RANet: Ranking Attention Network for Fast Video Object Segmentation
https://github.com/SysCV/idisc
공식 코드iDisc: Internal Discretization for Monocular Depth Estimation
https://github.com/TUTvision/Online-Panoptic-3D
공식 코드Online panoptic 3D reconstruction as a Linear Assignment Problem
https://github.com/Taekbum/category-nerf-reconstruction-official
공식 코드Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor Environment
https://github.com/TonyLianLong/RCF-UnsupVideoSeg
공식 코드Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
https://github.com/Twizwei/bddoia_project
공식 코드Explainable Object-induced Action Decision for Autonomous Vehicles
https://github.com/UkcheolShin/CRM_RGBTSeg
공식 코드Complementary Random Masking for RGB-Thermal Semantic Segmentation
https://github.com/VCIP-RGBD/DFormer
공식 코드DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
https://github.com/VCL3D/SingleShotCuboids
공식 코드https://github.com/VITA-Group/VideoLifter
공식 코드VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment
https://github.com/Va6lue/BST-Badminton-Stroke-type-Transformer
공식 코드BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
https://github.com/VinAIResearch/Open3DIS
공식 코드Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
https://github.com/Vision-CAIR/LTVRR
공식 코드Exploring Long Tail Visual Relationship Recognition with Large Vocabulary
https://github.com/Vision-CAIR/RelTransformer
공식 코드RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition
https://github.com/VisionTao/UOVOS
공식 코드Unsupervised Online Video Object Segmentation with Motion Property Understanding
https://github.com/Vujas-Eteph/CiVOS
공식 코드Revisiting Click-based Interactive Video Object Segmentation
https://github.com/Vujas-Eteph/READMem
공식 코드https://github.com/WU-CVGL/SIU3R
공식 코드SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
https://github.com/WaldJohannaU/3RScan
공식 코드RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
https://github.com/WeikaiTan/Toronto-3D
공식 코드Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
https://github.com/Westlake-AGI-Lab/Distill-Any-Depth
공식 코드Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator
https://github.com/XingangPan/SCNN
공식 코드Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/Xt-Chen/SARPN
공식 코드Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
https://github.com/XuMengyaAmy/CIDACaptioning
공식 코드Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation
https://github.com/XuefengBUPT/BS-Net
공식 코드Boundary-induced and scene-aggregated network for monocular depth prediction
https://github.com/YeLyuUT/MSDNet
공식 코드https://github.com/YingjieYin/Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation
공식 코드Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation
https://github.com/YorkUCVIL/Static-Dynamic-Interpretability
공식 코드https://github.com/ZENGXH/DMM_Net
공식 코드DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.