3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark (2412.07825v3)

Abstract: 3D spatial reasoning is the ability to analyze and interpret the positions, orientations, and spatial relationships of objects within the 3D space. This allows models to develop a comprehensive understanding of the 3D scene, enabling their applicability to a broader range of areas, such as autonomous navigation, robotics, and AR/VR. While large multi-modal models (LMMs) have achieved remarkable progress in a wide range of image and video understanding tasks, their capabilities to perform 3D spatial reasoning on diverse natural images are less studied. In this work we present the first comprehensive 3D spatial reasoning benchmark, 3DSRBench, with 2,772 manually annotated visual question-answer pairs across 12 question types. We conduct robust and thorough evaluation of 3D spatial reasoning capabilities by balancing the data distribution and adopting a novel FlipEval strategy. To further study the robustness of 3D spatial reasoning w.r.t. camera 3D viewpoints, our 3DSRBench includes two subsets with 3D spatial reasoning questions on paired images with common and uncommon viewpoints. We benchmark a wide range of open-sourced and proprietary LMMs, uncovering their limitations in various aspects of 3D awareness, such as height, orientation, location, and multi-object reasoning, as well as their degraded performance on images with uncommon camera viewpoints. Our 3DSRBench provide valuable findings and insights about the future development of LMMs with strong 3D reasoning capabilities. Our project page and dataset is available https://3dsrbench.github.io.

• The paper presents 3DSRBench, a benchmark with 2,772 annotated Q&A pairs to evaluate a wide range of 3D spatial reasoning challenges.
• It introduces FlipEval, a novel evaluation strategy that mitigates biases by comparing images with their horizontally flipped counterparts.
• Empirical tests show that LMMs lag significantly behind human performance, especially when handling diverse and uncommon camera viewpoints.

An Analysis of 3DSRBench: A Comprehensive Benchmark for 3D Spatial Reasoning in Large Multi-Modal Models

The paper "3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark" presents a methodically developed benchmark designed to comprehensively evaluate the 3D spatial reasoning capabilities of large multi-modal models (LMMs). The focus of this research is on advancing the understanding of how LMMs can manage 3D spatial reasoning tasks involving diverse natural images, a domain previously underexplored compared to 2D image and video analysis tasks commonly addressed by existing models.

Summary of Contributions

1. Benchmark Design and Dataset: The paper introduces 3DSRBench, a robust benchmark comprising 2,772 manually annotated visual question-answer pairs derived from 12 distinct question types. These queries are designed to cover a wide spectrum of 3D spatial reasoning challenges and include categories such as height, location, orientation, and multi-object reasoning.
2. FlipEval Strategy: The authors propose FlipEval, a novel evaluation strategy that counterbalances common biases observed in 3D spatial datasets by analyzing images along with their horizontally flipped counterparts. This approach ensures a fair assessment of models' spatial reasoning resilience across different spatial orientations.
3. Diversity in Viewpoints: The dataset includes both common and uncommon camera viewpoints to gauge the models' capabilities to adapt to varying perspectives—a critical aspect of robust 3D spatial reasoning. The data from the HSSD dataset allows for controlled testing conditions where images are rendered from known 3D viewpoints.
4. Empirical Evaluation: A wide array of open-source and proprietary LMMs, including alternatives like LLaVA and Cambrian-1, were evaluated using 3DSRBench. This evaluation revealed significant insights into their strengths and limitations concerning 3D reasoning tasks.

Key Findings

• Performance Lag Behind Human Baselines: The evaluation shows that despite advances in multi-modal systems, state-of-the-art models still fall significantly short of human-level performance, with notable lags especially in handling questions related to 3D orientations and complex spatial interactions.
• Degraded Performance with Uncommon Viewpoints: The performance of these models consistently degrades with uncommon camera viewpoints, emphasizing areas where LMMs require further refinement and adaptation for real-world applications.
• Insights from Mixed Encoder Architectures: The paper details experiments with mixed encoder architectures, demonstrating that integrating robust visual encoders like DINOv2 can enhance spatial reasoning abilities, particularly for capturing 3D detail and awareness.

Implications and Future Directions

The creation of 3DSRBench presents several implications for future research and application in areas requiring advanced 3D reasoning capabilities such as robotics, augmented reality, and autonomous vehicle navigation. It highlights the need for more efficient training strategies and model architectures that specifically target enhanced 3D comprehension.

Future research may focus on developing models with innate 3D awareness, achieved either through enhanced architectural innovations or enriched training datasets tailored for 3D environments. Additionally, insights from 3DSRBench can guide efforts in optimizing LLM outputs to better fuse visual and linguistic information for comprehensive scene understanding.

In conclusion, this benchmark serves as a pivotal resource for assessing and stimulating growth in the field of 3D spatial reasoning among LMMs, advocating for a shift towards more nuanced and perceptually aware AI systems.