NoPA replaces Gaussian object approximations with non-parametric distributions and MMD-based merging to improve accuracy in real-time 3D scene graph generation.
Dynam3d: Dynamic layered 3d tokens empower vlm for vision-and- language navigation.arXiv preprint arXiv:2505.11383
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
baseline 1polarities
baseline 1representative citing papers
HSGM structures 3D geometry and semantics into a multi-level map that lets VLMs perform high-level planning in zero-shot VLN, achieving SOTA on R2R-CE and RxR-CE.
GA-VLN builds a geometry-aware BEV representation from RGB-D inputs plus 3D foundation model features to deliver state-of-the-art vision-language navigation using only navigation data.
Semantic progress reasoning predicts instruction-style advancement from visual history to guide policies, yielding state-of-the-art success and efficiency on R2R-CE and RxR-CE.
StereoNav reaches new benchmark highs on R2R-CE and RxR-CE and improves real-robot reliability by supplying persistent target-location priors and stereo-derived geometry that stay stable under lighting changes and blur.
citing papers explorer
-
NoPA: Non-Parametric Online 3D Scene Graph Generation
NoPA replaces Gaussian object approximations with non-parametric distributions and MMD-based merging to improve accuracy in real-time 3D scene graph generation.
-
Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
HSGM structures 3D geometry and semantics into a multi-level map that lets VLMs perform high-level planning in zero-shot VLN, achieving SOTA on R2R-CE and RxR-CE.
-
GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation
GA-VLN builds a geometry-aware BEV representation from RGB-D inputs plus 3D foundation model features to deliver state-of-the-art vision-language navigation using only navigation data.
-
Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation
Semantic progress reasoning predicts instruction-style advancement from visual history to guide policies, yielding state-of-the-art success and efficiency on R2R-CE and RxR-CE.
-
What Limits Vision-and-Language Navigation ?
StereoNav reaches new benchmark highs on R2R-CE and RxR-CE and improves real-robot reliability by supplying persistent target-location priors and stereo-derived geometry that stay stable under lighting changes and blur.