3D Aware Region Prompted Vision Language Model
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
We present Spatial Region 3D (SR-3D) aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. We achieve this by enriching 2D visual features with 3D positional embeddings, which allows the 3D model to draw upon strong 2D priors for more accurate spatial reasoning across frames, even when objects of interest do not co-occur within the same view. Extensive experiments on both general 2D vision language and specialized 3D spatial benchmarks demonstrate that SR-3D achieves state-of-the-art performance, underscoring its effectiveness for unifying 2D and 3D representation space on scene understanding. Moreover, we observe applicability to in-the-wild videos without sensory 3D inputs or ground-truth 3D annotations, where SR-3D accurately infers spatial relationships and metric measurements.
Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 93 of its 104 references that are in the map. First posted on arXiv 16 Sep 2025.
Builds on
93 of 104 references.
Closest ancestors · 14
Same direction, newest first
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- 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models3 Jun
- 2024MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors2 May
- 2024SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors18 Mar
2 more
Other references · 79
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- 2020Scan2Cap: Context-aware Dense Captioning in RGB-D Scanscited by 218
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- 2024NVILA: Efficient Frontier Visual Language Modelscited by 209
50 most cited of 79 All 79 (JSON)
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Citing papers per year: 2025: 8; 2026: 25.
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