arXiv Computer Vision September 2, 2026 By Ziren Gong, Xiaohan Li, Fabio Tosi, Jiawei Han, Stefano Mattoccia, Jianfei Cai, Matteo Poggi

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

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arXiv Computer Vision

Aug 24

Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding

arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...

By Jie Xu, Na Zhao

llms agents computer-vision robotics

Hugging Face Trending Papers

Jul 30

MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians

Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features.

rag computer-vision nlp benchmarks

arXiv AI

Aug 13

CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D

arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.

By Mohamad Amin Mirzaei, Pantea Amoie, Ali Ekhterachian, Matin Mirzababaei, Babak Khalaj

llms rag computer-vision multimodal benchmarks

arXiv Machine Learning

Jul 3

RGB-Pointmap Pretraining for Unified 3D Scene Understanding

arXiv:2604. 02546v3 Announce Type: replace-cross Abstract: Pretraining 3D encoders through alignment with Contrastive Language-Image Pre-training (CLIP) has emerged as a promising direction for learning generalizable representations for 3D scene understanding.

By Ye Mao, Weixun Luo, Ranran Huang, Junpeng Jing, Krystian Mikolajczyk

llms nlp fine-tuning benchmarks safety

arXiv AI

Jul 24

3D-Aware VLMs with Implicit and Explicit Geometries

arXiv:2607. 21595v1 Announce Type: cross Abstract: Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning.

By Wenhao Li, Xueying Jiang, Quanhao Qian, Deli Zhao, Ran Xu, Shijian Lu, Gongjie Zhang

llms multimodal

arXiv Machine Learning

Jun 29

Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

arXiv:2604. 02546v2 Announce Type: replace-cross Abstract: Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding.