TL;DR
A mask-aware tri-modal framework that improves the quality of superpoint representations by retrieving a scene-level structural context from a pretrained PointSAM encoder to enhance object-centric evidence and predicting a soft mask weight to suppress unreliable superpoints.
For embodied agents to navigate and reason indoor spaces, they need object-level 3D representations that stay consistent over time as new frames arrive from a monocular camera. Current online 3D instance segmentation methods either depend on posed RGB-D input with ground-truth depth or couple tightly to the internal re...
OccBEV-Oracle, a lightweight completion neck inserted between the BEV backbone and dense head of a CenterPoint-style detector, and a direct measurement of where the neck edits the BEV map, show that the aligned GT-ROM mask itself supplies a strong localization prior.
This work investigates whether a frozen, self-supervised point transformer already contains the structural information required to isolate object instances without any handcrafted geometric prior, and develops a training-free segmenter that groups points via connected components on a key-similarity graph, using neither...
OVR-GS (Open-Vocabulary Removal in Gaussian Splatting), an instruction-driven object-removal framework for pre-trained 3D Gaussian Splatting (3DGS) scenes, demonstrates the effectiveness of localized Gaussian optimization for instruction-driven cleanup of reconstructed environments before visual inspection, presentatio...
This work enhances the existing iterative object-basesd visual localization approach with an additional semantic feature derived from a pretrained semantic segmentation model and conducts a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs.
Results indicate that incorporating textual semantic priors can effectively enhance high-level semantic representations of point clouds, providing a feasible solution for indoor 3D scene understand.
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