# ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition

arXiv: https://arxiv.org/abs/2507.11261 · HTML: https://mlsanity.com/paper/2507.11261/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 60 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](https://mlsanity.com/paper/1912.08830/) (2019)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [OpenChat: Advancing Open-source Language Models with Mixed-Quality Data](https://mlsanity.com/paper/2309.11235/) (2023)
- [Multi3DRefer: Grounding Text Description to Multiple 3D Objects](https://mlsanity.com/paper/2309.05251/) (2023)
- [Multi-View Transformer for 3D Visual Grounding](https://mlsanity.com/paper/2204.02174/) (2022)
- [Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds](https://mlsanity.com/paper/2112.08879/) (2021)
- [SAT: 2D Semantics Assisted Training for 3D Visual Grounding](https://mlsanity.com/paper/2105.11450/) (2021)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding](https://mlsanity.com/paper/2209.14941/) (2022)
- [LanguageRefer: Spatial-Language Model for 3D Visual Grounding](https://mlsanity.com/paper/2107.03438/) (2021)
- [Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving](https://mlsanity.com/paper/2310.02251/) (2023)
- [TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding](https://mlsanity.com/paper/2108.02388/) (2021)
- [PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation](https://mlsanity.com/paper/2305.19195/) (2023)
- [ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance](https://mlsanity.com/paper/2303.16894/) (2023)
- [Unifying 3D Vision-Language Understanding via Promptable Queries](https://mlsanity.com/paper/2405.11442/) (2024)
- [Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding](https://mlsanity.com/paper/2311.15383/) (2023)
- [Multi-modal Situated Reasoning in 3D Scenes](https://mlsanity.com/paper/2409.02389/) (2024)
- [SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis](https://mlsanity.com/paper/2304.14573/) (2023)
- [MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations](https://mlsanity.com/paper/2406.09401/) (2024)
- [Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations](https://mlsanity.com/paper/2305.06152/) (2023)
- [Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World](https://mlsanity.com/paper/2303.13233/) (2023)
- [Situational Awareness Matters in 3D Vision Language Reasoning](https://mlsanity.com/paper/2406.07544/) (2024)
- [MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding](https://mlsanity.com/paper/2403.03077/) (2024)

## Built on by (most cited first, 8 of 8)

- [LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation](https://mlsanity.com/paper/2604.11789/) (2026)
- [PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding](https://mlsanity.com/paper/2512.20907/) (2025)
- [UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing](https://mlsanity.com/paper/2603.08131/) (2026)
- [OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding](https://mlsanity.com/paper/2512.23020/) (2025)
- [PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding](https://mlsanity.com/paper/2606.31148/) (2026)
- [Where to Look Matters: Learning Influential Views for VLM-based 3D Visual Grounding](https://mlsanity.com/paper/2609.04741/) (2026)
- [CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding](https://mlsanity.com/paper/2609.19911/) (2026)
- [DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians](https://mlsanity.com/paper/2610.00040/) (2026)

## Abstract (arXiv)

3D visual grounding aims to identify and localize objects in a 3D space based on textual descriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsistencies in spatial descriptions caused by perspective variations. To tackle these challenges, we propose ViewSRD, a framework that formulates 3D visual grounding as a structured multi-view decomposition process. First, the Simple Relation Decoupling (SRD) module restructures complex multi-anchor queries into a set of targeted single-anchor statements, generating a structured set of perspective-aware descriptions that clarify positional relationships. These decomposed representations serve as the foundation for the Multi-view Textual-Scene Interaction (Multi-TSI) module, which integrates textual and scene features across multiple viewpoints using shared, Cross-modal Consistent View Tokens (CCVTs) to preserve spatial correlations. Finally, a Textual-Scene Reasoning module synthesizes multi-view predictions into a unified and robust 3D visual grounding. Experiments on 3D visual grounding datasets show that ViewSRD significantly outperforms state-of-the-art methods, particularly in complex queries requiring precise spatial differentiation. Code is available at https://github.com/visualjason/ViewSRD.