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Object SLAM
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SLAM (Simultaneous Localisation and Mapping) at the level of object
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting
TextSLAM: Visual SLAM with Semantic Planar Text Features
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects
Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation
SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
Object SLAM-Based Active Mapping and Robotic Grasping
Compositional Scalable Object SLAM
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
TextSLAM: Visual SLAM with Planar Text Features
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
CubeSLAM: Monocular 3D Object SLAM
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
Constructing Category-Specific Models for Monocular Object-SLAM
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
Real-time Monocular Object SLAM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.