SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models (2406.01584v3)

Abstract: Vision LLMs (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities. SpatialRGPT advances VLMs' spatial understanding through two key innovations: (1) a data curation pipeline that enables effective learning of regional representation from 3D scene graphs, and (2) a flexible plugin module for integrating depth information into the visual encoder of existing VLMs. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Additionally, we propose SpatialRGBT-Bench, a benchmark with ground-truth 3D annotations encompassing indoor, outdoor, and simulated environments, for evaluating 3D spatial cognition in VLMs. Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts. The model also exhibits strong generalization capabilities, effectively reasoning about complex spatial relations and functioning as a region-aware dense reward annotator for robotic tasks. Code, dataset, and benchmark are released at https://www.anjiecheng.me/SpatialRGPT

• The paper introduces a novel framework that generates precise 3D scene graphs from 2D images to enhance spatial reasoning in VLMs.
• It employs open-vocabulary detection, segmentation, and metric depth estimation to construct canonical 3D scene graphs for robust spatial understanding.
• Experimental results on the SpatialRGPT-Bench benchmark demonstrate significant performance improvements in spatial QA tasks relevant to robotics and AR.

SpatialRGPT: Grounded Spatial Reasoning in Vision LLMs

Introduction and Background

The paper "SpatialRGPT: Grounded Spatial Reasoning in Vision LLMs" (2406.01584) presents a framework that fundamentally enhances Vision LLMs (VLMs) in comprehending spatial arrangements within visual environments. Despite VLMs excelling in tasks like image classification, captioning, and document parsing, their capacity for spatial reasoning has been notably insufficient, evident struggles encompass not only basic spatial concepts like relative directions but also extend to complex relations vital for applications in robotics and augmented reality.

Building upon advancements like SpatialVLM, which utilizes a data generation pipeline for large-scale training with spatially-aware Visual Question Answering (VQA), SpatialRGPT seeks to overcome two core challenges obstructing effective spatial reasoning: the precise parsing of regional information for object instances and the reliance on RGB data for perceiving spatial relations, which necessitates 3D inputs such as depth data.

Methodology

Data Curation and 3D Spatial Representation

SpatialRGPT introduces a sophisticated data pipeline that generates accurate 3D scene graphs from 2D images, enabling enhanced spatial reasoning. This process includes:

1. Open-Vocabulary Detection and Segmentation: Leveraging advanced models like Grounding Dino for bounding box detection and SAM-HQ for precise segmentation, ensuring robust mask proposals.
2. Metric Depth Estimation: Utilizing Metric3Dv2, known for its training on diverse scenes, combined with WildCamera for intrinsic matrix estimation.
3. Canonical 3D Scene Graph Construction: Creating 3D scene graphs where nodes symbolize object instances and edges denote spatial relations, including direction and distance metrics.

These comprehensive annotations facilitate spatial QA tasks via both template-based and LLM-based approaches, providing VLMs with requisite spatial knowledge for complex reasoning.

Enhancing Visual Encoder Architecture

The novel architecture of SpatialRGPT includes a region representation module and a flexible plugin module capable of integrating relative-depth information into the visual encoder. This approach leverages depth inputs to substantially improve spatial reasoning capabilities without impairing the effectiveness of existing RGB-trained visual encoders.

Experimental Results

The paper introduces a novel benchmark, SpatialRGPT-Bench, endowed with ground-truth 3D annotations across diverse environments such as indoor, outdoor, and simulated spaces. SpatialRGPT demonstrates substantial enhancement in spatial reasoning performance, effectively generalizing to address complex spatial relations.

In practical applications, SpatialRGPT serves as a region-aware dense reward annotator for robotics, showcasing its ability to function as a robust complex spatial reasoner without reliance on external systems like GPT-4.

Implications and Future Work

The implications of SpatialRGPT are far-reaching, promising advancements in robotics navigation and manipulation, as well as augmented reality applications requiring precise spatial awareness. Future developments could explore integrating pose estimation methods or refining evaluation techniques for spatial VLMs, addressing ongoing challenges in achieving finer granularity in spatial comprehension.

In conclusion, SpatialRGPT signifies a substantial leap in the spatial reasoning capabilities of VLMs, offering profound theoretical and practical contributions to artificial intelligence applications reliant on spatial cognition. The release of this framework and its accompanying dataset promises to catalyze further innovation within the research community, nurturing continued exploration into immersive and real-world spatial AI.