3AM integrates MUSt3R 3D features into SAM2 via a Feature Merger and FOV-aware sampling to deliver geometry-consistent video object segmentation from RGB alone, with large gains on wide-baseline datasets.
Nesf: Neural semantic fields for generalizable semantic segmentation of 3d scenes
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
OpenTrack3D achieves state-of-the-art open-vocabulary 3D instance segmentation by generating cross-view consistent proposals online with a visual-spatial tracker and replacing CLIP with an MLLM for improved compositional reasoning.
FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.
citing papers explorer
-
3AM: 3egment Anything with Geometric Consistency in Videos
3AM integrates MUSt3R 3D features into SAM2 via a Feature Merger and FOV-aware sampling to deliver geometry-consistent video object segmentation from RGB alone, with large gains on wide-baseline datasets.
-
OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation
OpenTrack3D achieves state-of-the-art open-vocabulary 3D instance segmentation by generating cross-view consistent proposals online with a visual-spatial tracker and replacing CLIP with an MLLM for improved compositional reasoning.
-
First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction
FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.