mlsanityData to 14 Oct 2026

Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos

arXiv 2507.22052 ↗First posted 29 Jul 2025cs.CVcited by 10

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Abstract (arXiv)

We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The system features two key components: CLIP3R, a CLIP-informed 3D reconstruction module that predicts dense point maps from overlapping clips while embedding object-level semantics; and 2D-3D OVS, a 2D-3D open-vocabulary semantic module that lifts 2D features into 3D by learning fused descriptors integrating spatial, geometric, and semantic cues. Unlike prior methods, Ov3R incorporates CLIP semantics directly into the reconstruction process, enabling globally consistent geometry and fine-grained semantic alignment. Our framework achieves state-of-the-art performance in both dense 3D reconstruction and open-vocabulary 3D segmentation, marking a step forward toward real-time, semantics-aware Spatial AI.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 53 of its 69 references that are in the map. First posted on arXiv 29 Jul 2025.

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53 of 69 references.

References · 53

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 8.

10 papers.