3D Visual Grounding

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23 papers in the last four weeks, up 92% on the four weeks before. 0.2% of all new papers.

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CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
Apr 21, 2026Shengli Zhou, Xiangchen Wang, Guanhua Chen +13D Spatial Reasoning VLM Reasoning
1Southern University of Science and Technology · 2SpatialTemporal AI
UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Affordance Segmentation
Mar 24, 2026Jiaying Lin, Dan Xu3D Scene Understanding 3D Visual Grounding
The Hong Kong University of Science and Technology
X-GS: An Extensible Framework for Perceiving and Thinking with 3D Gaussian Splatting
Mar 10, 2026Yueen Ma, Zenglin Xu, Irwin King3D Gaussian Splatting 3DGS SLAM
The Chinese University of Hong Kong · Shanghai Academy of AI for Science · Fudan University
SplatReasoner: Enhancing Embodied Reasoning and Grounding by Novel View Synthesis
Jan 19, 2026Kim Yu-Ji, Dahye Lee, Kim Jun-Seong +6VLM Reasoning Visually Grounded Reasoning
POSTECH · KAIST · ETRI +1
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Jan 14, 2026Yang Xing, Jiong Wu, Savas Ozdemir +4Interactive Medical Image Segmentation Unified Multimodal Models
Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA · Department of Radiology, University of Florida, Jacksonville, FL, USA · Research Computing, University of Florida, Gainesville, FL, USA +2
OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding
Dec 28, 2025Wenyuan Huang, Zhenyu Zhang, Zhao Wang +43D VLMs 3D Visual Grounding
Nanjing University, School of Intelligent Science and Technology, Nanjing, China · China Mobile Zijin Innovation Institute, Nanjing, China
PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding
Dec 24, 2025Seongmin Jung, Seongho Choi, Gunwoo Jeon +23D Scene Representation 3D Visual Grounding
Seoul National University, South Korea · Robotics Lab, Hyundai Motor Company, South Korea · Pohang University of Science and Technology (POSTECH), South Korea
Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics
Dec 15, 2025Enshen Zhou, Yibo Li, Jingkun An +123D Spatial Reasoning VLMs for Robotics
School of Software, Beihang University · Beijing Academy of Artificial Intelligence · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University +2
Error-Driven Scene Editing for 3D Grounding in Large Language Models
Nov 18, 2025Yue Zhang, Zun Wang, Han Lin +53D Scene Editing 3D Spatial Reasoning
UNC Chapel Hill · University of Michigan · Google Research
Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning
Aug 3, 2025Hanqing Wang, Zhenhao Zhang, Kaiyang Ji +12Human-Object Interaction 3D Visual Grounding
The Hong Kong University of Science and Technology, Guangzhou, China · ShanghaiTech University, Shanghai, China · Zhejiang University, Hangzhou, China +3
Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Jun 13, 2025Junha Lee, Eunha Park, Chunghyun Park +2Synthetic Data Generation for Robotics Embodied AI
Pohang University of Science and Engineering (POSTECH) · SqueezeBits · RLWLRD

Apr 21, 2026, cs.CLJ/K move · Enter open · S save

CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models

Shengli Zhou, Xiangchen Wang, Guanhua Chen, Feng Zheng

1Southern University of Science and Technology · 2SpatialTemporal AI

3D Spatial Reasoning VLM Reasoning 3D VLMs 3D Visual Grounding

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Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: preserving spatial relations that are most pertinent to the specific 3D-VL task. Guided by this insight, we propose the Conceptual-Adjacent Scene Graph Pruner (CAPruner). CAPruner integrates fuzzy semantic relevance with spatial proximity to estimate the importance of relations, enabling the selection of critical relations in a task-specific context. Moreover, to avoid costly relation-level annotations, CAPruner is trained by supervising the aggregated scores of each node's incident edges. Extensive experiments demonstrate that CAPruner effectively preserves relations essential for spatial reasoning, leading to substantial performance improvements of LLMs on 3D-VL tasks. Code is available at https://github.com/fz-zsl/CAPruner.