ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
Although great progress has been made in 3D visual grounding, current models still rely on explicit textual descriptions for grounding and lack the ability to reason human intentions from implicit instructions. We propose a new task called 3D reasoning grounding and introduce a new benchmark ScanReason which provides over 10K question-answer-location pairs from five reasoning types that require the synerization of reasoning and grounding. We further design our approach, ReGround3D, composed of the visual-centric reasoning module empowered by Multi-modal Large Language Model (MLLM) and the 3D grounding module to obtain accurate object locations by looking back to the enhanced geometry and fine-grained details from the 3D scenes. A chain-of-grounding mechanism is proposed to further boost the performance with interleaved reasoning and grounding steps during inference. Extensive experiments on the proposed benchmark validate the effectiveness of our proposed approach.
Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 43 of its 52 references that are in the map. First posted on arXiv 1 Jul 2024.
Builds on
43 of 52 references.
Closest ancestors · 3
Same direction, newest first
- 2023LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning30 Nov
- 2023CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding10 Oct
- 2022SQA3D: Situated Question Answering in 3D Scenes14 Oct
Other references · 40
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- 2023MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningcited by 617
- 2019ScanRefer: 3D Object Localization in RGB-D Scans using Natural Languagecited by 446
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- 2021ScanQA: 3D Question Answering for Spatial Scene Understandingcited by 340
- 2023PointLLM: Empowering Large Language Models to Understand Point Cloudscited by 326
- 2022Mask3D: Mask Transformer for 3D Semantic Instance Segmentationcited by 299
- 2020Scan2Cap: Context-aware Dense Captioning in RGB-D Scanscited by 215
- 20233D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignmentcited by 201
- 2023Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifierscited by 170
- 2023EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AIcited by 146
- 2023ImageBind-LLM: Multi-modality Instruction Tuningcited by 142
- 2022Multi-View Transformer for 3D Visual Groundingcited by 138
- 2023LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agentcited by 134
- 2021Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Cloudscited by 128
- 2023X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languagescited by 127
- 2021SAT: 2D Semantics Assisted Training for 3D Visual Groundingcited by 124
- 2022Language Conditioned Spatial Relation Reasoning for 3D Object Groundingcited by 121
- 20223D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selectioncited by 109
- 2021LanguageRefer: Spatial-Language Model for 3D Visual Groundingcited by 88
- 2023End-to-End 3D Dense Captioning with Vote2Cap-DETRcited by 77
- 2022X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioningcited by 70
- 2022UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Groundingcited by 69
- 2023Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Modelscited by 59
- 2019Linguistic generalization and compositionality in modern artificial neural networkscited by 58
- 2022MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenescited by 46
- 20213D Question Answeringcited by 41
- 2022Spatiality-guided Transformer for 3D Dense Captioning on Point Cloudscited by 40
- 2023M3DBench: Let's Instruct Large Models with Multi-modal 3D Promptscited by 32
- 2023Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detectioncited by 24
- 2022Contextual Modeling for 3D Dense Captioning on Point Cloudscited by 18
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Citing papers per year: 2024: 8; 2025: 30; 2026: 9.
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