RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction

Abstract: Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving plausible disentanglement, the performance drops significantly when processing the indoor scenes where objects are usually partially observed. We propose RICO to address this by regularizing the unobservable regions for indoor compositional reconstruction. Our key idea is to first regularize the smoothness of the occluded background, which then in turn guides the foreground object reconstruction in unobservable regions based on the object-background relationship. Particularly, we regularize the geometry smoothness of occluded background patches. With the improved background surface, the signed distance function and the reversedly rendered depth of objects can be optimized to bound them within the background range. Extensive experiments show our method outperforms other methods on synthetic and real-world indoor scenes and prove the effectiveness of proposed regularizations. The code is available at https://github.com/kyleleey/RICO.

• The paper introduces RICO, which regularizes unobservable indoor regions to enhance 3D compositional reconstruction.
• It leverages the foreground-background relationship to ensure smoother geometry in occluded background areas.
• Extensive experiments in synthetic and real-world scenes demonstrate RICO's superior performance over traditional methods.

The paper "RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction" addresses challenges in neural implicit surfaces, particularly for multi-view reconstruction in indoor scenes. Traditional approaches often suffer from performance drops when dealing with indoor environments, where objects are frequently partially observed. To tackle this problem, the authors introduce RICO, a novel method designed to enhance compositional reconstruction by regularizing unobservable regions.

Key Ideas

1. Foreground-Background Relationship:
   • The essence of RICO lies in leveraging the relationship between foreground objects and the background scene. By focusing on the geometry of occluded background patches, the method aims to implicitly guide the reconstruction of foreground objects that are partially hidden.
2. Regularizing Geometry Smoothness:
   • A crucial component of RICO is the regularization of the background's surface geometry. This approach ensures that the occluded background regions are smooth, which is a key factor in achieving better 3D reconstruction of objects located in these regions.
3. Signed Distance Function and Depth Optimization:
   • With a refined background surface, the paper states that the Signed Distance Function (SDF) and reverse-rendered depth of objects are optimized. This ensures that the object reconstructions fit well within the contextual bounds provided by the background, thereby reducing artifacts and enhancing overall quality.

Contributions and Results

• The paper conducts extensive experiments on both synthetic and real-world indoor scenes to validate their approach. The results demonstrate that RICO substantially outperforms existing methods, particularly in challenging scenarios with significant occlusions.
• The regularization techniques proposed offer significant improvements in the smoothness and accuracy of reconstructed scenes, making RICO a notable advancement in indoor compositional reconstruction.

Practical Applications

• Scene Editing and Manipulation:
  • The ability to handle partially occluded indoor scenes effectively makes RICO particularly useful for practical applications such as scene editing and manipulation. This has implications for areas such as augmented reality, virtual reality, and interactive graphics.
• Generalization to Real-World Data:
  • Another strength of RICO is its robustness across different types of data. By demonstrating effectiveness in both controlled synthetic environments and complex real-world scenarios, the method shows promise for broader adoption in various applications requiring precise 3D reconstruction.

Through its innovative approach to regularizing unobservable regions, RICO advances the state-of-the-art in indoor scene reconstruction, enhancing both the smoothness of background modeling and the accuracy of foreground object placement. The code for this method is open-sourced and available for further exploration and use by the research community.