ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities (2407.01525v3)

Abstract: 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 LLM (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.

Insights on "ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities"

The paper "ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities" presents a novel approach in the domain of 3D visual grounding, extending current methodologies to encompass reasoning tasks that involve implicit human instructions. The authors propose a new benchmark, ScanReason, and introduce the task of "3D reasoning grounding" that necessitates models to jointly employ reasoning and grounding to predict object locations in 3D environments.

Methodology

The authors detail the ReGround3D framework, which leverages a visual-centric reasoning module augmented by Multi-modal LLMs (MLLM). This system is complemented by a 3D grounding module that captures enhanced geometric and fine-grained details from 3D environments. A key innovation of the framework is the Chain-of-Grounding mechanism that interleaves reasoning and grounding tasks, enhancing the inference process through iterative refinement.

ReGround3D operates by first conducting reasoning on language instructions and the visual environment, identifying key aspects of the 3D scene required for object location prediction. This reasoning process is enhanced by a 3D grounding module that supports spatial reasoning about target objects’ locations, and employs a query selection mechanism to refine grounding predictions using cross-attention.

Benchmark and Data Annotation

The ScanReason benchmark integrates several types of 3D reasoning including spatial, functional, logical, emotional, and safety reasoning. The benchmark comprises a comprehensive dataset with over 10,000 question-answer-3D bounding box pairs from 2,000 scenes. A notable aspect of this work is the use of GPT-4 for data annotation, significantly enhancing dataset creation efficiency. This dataset facilitates evaluating models' ability to handle complex and implicit instructions, a marked departure from conventional 3D visual grounding tasks that rely on explicit textual descriptions.

Results

Empirical results showcase the robust performance of ReGround3D on the ScanReason benchmark across various reasoning categories. The model achieves notable accuracy improvements, particularly with the Chain-of-Grounding mechanism that iteratively refines reasoning and grounding decisions. Comparative analysis against existing systems like 3D-LLM and Chat-3D v2 demonstrates that ReGround3D outperforms these methods, particularly on spatial and logical reasoning tasks.

Implications and Future Directions

The research presented in this work has significant implications for applications in robotics and augmented reality, where seamless human-agent interaction is crucial. By enabling systems to interpret and act on human-like reasoning tasks, this research paves the way for the development of sophisticated 3D environments' understanding.

From a theoretical standpoint, the introduction of the 3D reasoning grounding task broadens the scope of traditional visual grounding models, pushing towards more comprehensive scene understanding frameworks. Practically, the proposed methodologies offer pathways for advancing autonomous navigation and interaction systems capable of understanding nuanced human instructions.

Looking forward, further exploration could address the overlapping questions noted in the benchmark’s high-level reasoning categories. Enhancements in semantic understanding and more nuanced response mechanisms may strengthen systems' ability to differentiate between closely related queries. Additionally, integrating other sensory inputs beyond visual and textual data could enrich models' interaction capacities, setting the stage for more holistic embodied agent developments.

In summary, the paper contributes substantially to the field of 3D vision and language learning by successfully merging language comprehension with visual perception, enabling robust reasoning and grounding in complex 3D scenes.