arXiv AI September 10, 2026 By Thodoris Betsas, Anastasios Doulamis, Andreas Georgopoulos

GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.

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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

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arXiv Computer Vision

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AffordAny: Open-World 3D Affordance Grounding from Monocular RGB Images via Vision-Language-Guided Geometric Reasoning

arXiv:2608.20720v1 Announce Type: new Abstract: Open-world 3D affordance grounding requires localizing functional object parts in 3D given free-form language queries. Existing methods typically assum...

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arXiv AI

Jul 31

TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions

arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.

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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.

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arXiv AI

Aug 25

ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting

ExtrinSplat is a new framework that separates geometry from semantics in 3D Gaussian Splatting scenes. It clusters Gaussians into overlapping 3D object groups and uses a Vision‑Language Model to generate lightweight textual hypotheses, creating an extrinsic index layer that handles complex polysemy. This approach reduces adaptation time from hours to minutes, cuts storage overhead by orders of magnitude, and outperforms existing embedding‑based methods on open‑vocabulary 3D object selection and semantic segmentation benchmarks.

By Jiayu Ding, Xinpeng Liu, Zhiyi Pan, Shiqiang Long, Ge Li

llms rag computer-vision multimodal benchmarks

arXiv AI

Jun 19

QueryGaussian: Scalable and Training-Free Open-Vocabulary 3D Instance Retrieval

arXiv:2606. 19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis.

By Xiuyuan Zhu, Ke Lu, Zijie Yang, Chao Yue, Jian Xue, Dongming Zhang

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Related stories

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

AffordAny: Open-World 3D Affordance Grounding from Monocular RGB Images via Vision-Language-Guided Geometric Reasoning

TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions

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

ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting

QueryGaussian: Scalable and Training-Free Open-Vocabulary 3D Instance Retrieval