FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction (2410.08282v1)

Abstract: Humans effortlessly integrate common-sense knowledge with sensory input from vision and touch to understand their surroundings. Emulating this capability, we introduce FusionSense, a novel 3D reconstruction framework that enables robots to fuse priors from foundation models with highly sparse observations from vision and tactile sensors. FusionSense addresses three key challenges: (i) How can robots efficiently acquire robust global shape information about the surrounding scene and objects? (ii) How can robots strategically select touch points on the object using geometric and common-sense priors? (iii) How can partial observations such as tactile signals improve the overall representation of the object? Our framework employs 3D Gaussian Splatting as a core representation and incorporates a hierarchical optimization strategy involving global structure construction, object visual hull pruning and local geometric constraints. This advancement results in fast and robust perception in environments with traditionally challenging objects that are transparent, reflective, or dark, enabling more downstream manipulation or navigation tasks. Experiments on real-world data suggest that our framework outperforms previously state-of-the-art sparse-view methods. All code and data are open-sourced on the project website.

• The paper introduces FusionSense, a framework that fuses common sense, vision, and tactile data for robust sparse-view 3D reconstruction.
• The paper employs a hierarchical optimization strategy using 3D Gaussian Splatting and hull pruning to enhance both global shape and local details.
• The paper demonstrates improved reconstruction performance with fewer tactile interactions, validated on challenging real-world objects using metrics like PSNR and SSIM.

Overview of "FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction"

This paper introduces "FusionSense," a novel framework designed to enhance robotic perception through efficient 3D reconstruction by integrating common sense, vision, and tactile input. The framework addresses long-standing challenges in sparse-view 3D reconstruction, focusing on combining the strengths of each modality to improve the accuracy and robustness of the reconstructed scenes or objects.

Key Contributions

The authors present several innovations:

1. Integration of Multimodal Sensory Inputs: FusionSense leverages foundation models to integrate common sense priors with sparse sensory data from both vision and tactile sensors. This integration enables effective handling of complex objects that are typically problematic for 3D reconstruction, such as transparent, reflective, and dark surfaces.
2. Hierarchical Optimization Strategy: The framework employs 3D Gaussian Splatting (3DGS) for efficient scene representation. A hierarchical optimization process ensures robust global shape representation and local geometric refinement. Hull pruning is introduced to eliminate artifacts that obstruct optimal scene understanding, leading to better scene and object representations.
3. Active Touch Point Selection: The framework incorporates an active strategy to identify key tactile points, focusing on regions with substantial geometric change. This strategy reduces the number of required tactile interactions while increasing the detail captured in the reconstruction.

Methodological Insights

FusionSense is constructed upon three core modules:

• Robust Global Shape Representation: It initializes using hybrid priors from monocular depth estimation and visual hull information. Such initialization ensures consistent geometry across multiple views, which is crucial for handling sparse data inputs effectively.
• Active Touch Selection: Utilizes geometric gradient data and vision-LLMs to rank potential tactile points strategically. This prioritization is based on both structural complexity and semantic relevance, minimizing unnecessary interactions.
• Local Geometric Optimization: Introduces anchor Gaussians from tactile contacts to refine local details. This step is vital for enhancing the precision of the reconstructed model, leveraging fine tactile data to inform the overall geometric representation.

Experimental Validation

The authors validate FusionSense using real-world robotic setups involving challenging objects. Quantitative results demonstrate superior performance in novel view synthesis (as measured by PSNR and SSIM) compared to other state-of-the-art methods. The framework also shows competitive results in Chamfer Distance for object reconstruction, even with fewer tactile interactions.

The inclusion of automated hull pruning and an effective fusion of tactile inputs significantly contributes to the model's enhanced performance. This framework efficiently reconstructs scenes and objects under sparse-view conditions, which is a frequent limitation of traditional methods.

Implications and Future Directions

The introduction of FusionSense offers several implications for robotic perception:

• Enhancement of Robotic Tasks: With more robust 3D reconstructions, robotic manipulation and navigation tasks in complex environments can become more reliable and efficient.
• Reduction in Data Requirements: By optimizing the information gained from sparse inputs, FusionSense reduces dependency on dense data, aligning with the needs of real-world applications where exhaustive data collection is impractical.

Looking ahead, further development could include automating robotic control for tactile data acquisition and expanding the integration of AI-driven semantic insights to further inform and refine the reconstruction process. Exploration into dynamic scenarios where objects are mobile during reconstruction could also be a potential avenue for expanding the framework’s applicability.

FusionSense represents a significant move towards more holistic robotic perception systems, setting a precedent for future research integrating multimodal sensory data with common sense reasoning.