SSR: Enhancing Depth Perception in Vision-Language Models via Rationale-Guided Spatial Reasoning (2505.12448v2)

Abstract: Despite impressive advancements in Visual-LLMs (VLMs) for multi-modal tasks, their reliance on RGB inputs limits precise spatial understanding. Existing methods for integrating spatial cues, such as point clouds or depth, either require specialized sensors or fail to effectively exploit depth information for higher-order reasoning. To this end, we propose a novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that transforms raw depth data into structured, interpretable textual rationales. These textual rationales serve as meaningful intermediate representations to significantly enhance spatial reasoning capabilities. Additionally, we leverage knowledge distillation to compress the generated rationales into compact latent embeddings, which facilitate resource-efficient and plug-and-play integration into existing VLMs without retraining. To enable comprehensive evaluation, we introduce a new dataset named SSR-CoT, a million-scale visual-language reasoning dataset enriched with intermediate spatial reasoning annotations, and present SSRBench, a comprehensive multi-task benchmark. Extensive experiments on multiple benchmarks demonstrate SSR substantially improves depth utilization and enhances spatial reasoning, thereby advancing VLMs toward more human-like multi-modal understanding. Our project page is at https://yliu-cs.github.io/SSR.

Enhancing Depth Perception in Vision-LLMs via Rationale-Guided Spatial Reasoning

Recent advancements in Vision-LLMs (VLMs) have unlocked significant potential for various multi-modal tasks, effectively bridging image processing and natural language understanding. However, a lingering limitation in these models is their dependence on RGB inputs, which often fail to capture intricate spatial relationships within complex scenes. Addressing this, the paper "SSR: Enhancing Depth Perception in Vision-LLMs via Rationale-Guided Spatial Reasoning" proposes an innovative framework designed to integrate structured depth information into VLMs, thus enhancing their spatial reasoning capabilities.

The central contribution of this work is the spatial sense and reasoning (SSR) methodology, which transforms raw depth data into structured textual rationales. These rationales serve as meaningful intermediate representations, enabling VLMs to achieve more precise spatial understanding. The SSR model utilizes a depth encoder to extract depth features and subsequently maps these features into a rationale language, which aligns with the semantic embeddings of existing LLMs. This transformation is facilitated by a rationale-guided knowledge distillation process, compressing the rich depth information into compact latent embeddings for efficient integration into VLMs. The primary advantage of this approach is its ability to enhance spatial reasoning without necessitating significant retraining of the models.

To evaluate SSR's effectiveness, the authors introduce a new dataset and a comprehensive multi-task benchmark that includes spatial reasoning annotations. Experiments across these benchmarks reveal that the SSR model substantively improves VLMs' ability to utilize depth information, thereby significantly advancing their spatial reasoning capabilities. This improvement reflects SSR's potential to approach a more human-like comprehension of multi-modal content.

Several notable quantitative outcomes underscore the benefits of this approach. For instance, SSR leads to remarkable enhancements in spatial tasks such as object positioning and distance estimation. These improvements signify a crucial stride towards enriching the spatial intelligence of VLMs, particularly beneficial in fields like robotics and autonomous systems where spatial understanding is paramount.

The implications of this research are manifold. Practically, enhancing the depth perception of VLMs could revolutionize applications requiring spatial reasoning, like autonomous navigation, robotic manipulation, and augmented reality. Theoretically, this work contributes to the broader domain of making AI systems more adept at human-like reasoning by integrating additional sensory data and processing layers.

Looking ahead, this paper paves the way for further exploration into hybrid learning models that effectively combine multi-dimensional data. Future research might explore optimizing the efficiency of such integrations or applying SSR principles in real-time systems with stringent computational constraints. As AI continues to evolve, methodologies like SSR that broaden the understanding capabilities of VLMs will be instrumental in bridging current gaps in machine perception and reasoning.