https://github.com/LeeBY68/Hier-SLAM
공식 코드Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian Splatting
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 3
- 보존된 평가 기록
- 5
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/LeiWangR/LSFNet
공식 코드Loss Switching Fusion with Similarity Search for Video Classification
https://github.com/LifengFan/Triadic-Belief-Dynamics
공식 코드Learning Triadic Belief Dynamics in Nonverbal Communication from Videos
https://github.com/LingyiHongfd/LVOS
공식 코드https://github.com/LiuZhu-CV/BDLFusion
공식 코드Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
https://github.com/MECLabTUDA/SASVi
공식 코드https://github.com/MMCheng/SSPC-Net
공식 코드SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
https://github.com/MSiam/AdaptiveMaskedProxies
공식 코드https://github.com/MSiam/motion_adaptation
공식 코드https://github.com/MegviiDetection/video_analyst
공식 코드https://github.com/MohsenFayyaz89/STFCN
공식 코드https://github.com/MrZihan/NavRAG
공식 코드NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM
https://github.com/MuchHair/PD-Net
공식 코드Polysemy Deciphering Network for Robust Human-Object Interaction Detection
https://github.com/NUST-Machine-Intelligence-Laboratory/HFAN
공식 코드Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
https://github.com/NVlabs/GaussianSTORM
공식 코드STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
https://github.com/NVlabs/GroupViT
공식 코드GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/NVlabs/learningrigidity
공식 코드Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
https://github.com/NanboLi/MulMON
공식 코드Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
https://github.com/Nathan-UW/OpenGF
공식 코드https://github.com/NeurAI-Lab/G2S
공식 코드Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
https://github.com/NeurAI-Lab/UniNet
공식 코드https://github.com/Nguyendat-bit/U-net
공식 코드ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/NiFangBaAGe/DATTT
공식 코드Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
https://github.com/OpenMask3D/openmask3d
공식 코드https://github.com/OpenRobotLab/Grounded_3D-LLM
공식 코드https://github.com/POSTECH-CVLab/InstaOrder
공식 코드https://github.com/PRBonn/semantic-kitti-api
공식 코드SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/PaddlePaddle/PaddleSeg
공식 코드EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle
https://github.com/Pointcept/OpenIns3D
공식 코드OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
https://github.com/Qi-Zhangyang/GPT4Scene
공식 코드GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models
https://github.com/QianWangX/VidSeg_diffusion
공식 코드Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models
https://github.com/QingyongHu/SensatUrban
공식 코드Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
https://github.com/Qualcomm-AI-research/delta-distillation
공식 코드https://github.com/RobertLuo1/NeurIPS2023_SOC
공식 코드SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
https://github.com/SHI-Labs/Mask-Selection-Networks
공식 코드MSN: Efficient Online Mask Selection Network for Video Instance Segmentation
https://github.com/Scatteredrain/SALI
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.