HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization (2302.14340v2)

Abstract: Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally optimizing the local, pixel-wise correspondences across multiple views. We are thus motivated to make use of the complementary benefits from the two strategies, and propose a method termed Helix-shaped neural implicit Surface learning or HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the guidance to regularize the learning of the other one, and conducts such intertwined regularization iteratively during the learning process. We also propose an efficient scheme for differentiable volume rendering in HelixSurf. Experiments on surface reconstruction of indoor scenes show that our method compares favorably with existing methods and is orders of magnitude faster, even when some of existing methods are assisted with auxiliary training data. The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf.

• The paper introduces a double-helix iterative regularization that fuses neural implicit surface learning with multi-view stereo to enhance reconstruction accuracy.
• It achieves efficient training through dynamic volume rendering using evolving occupancy grids, significantly reducing computational load.
• It overcomes challenges in textureless areas by leveraging superpixel-based regularization, ensuring smooth and detailed indoor reconstructions.

Overview of "HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization"

The paper "HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization" introduces an innovative approach to indoor scene reconstruction from multi-view imagery, integrating the strengths of neural implicit surface learning with traditional multi-view stereo (MVS) methods. The method, termed HelixSurf, leverages an intertwined regularization strategy that iteratively enhances the scene's geometric recovery accuracy and computational efficiency.

Key Contributions

1. Intertwined Regularization Strategy: The core of HelixSurf's methodology involves a double-helix shaped iterative regularization process. It harnesses intermediate predictions from both neural implicit surface learning and MVS to guide and improve each other’s optimization over iterations. This process significantly boosts the quality of scene reconstruction.
2. Efficient Differentiable Volume Rendering: The paper proposes an advanced scheme for rendering that utilizes dynamic occupancy grids, substantially reducing the computational load associated with conventional volume rendering methods in neural surface reconstruction, achieving orders of magnitude reduction in training time.
3. Handling Textureless Surface Areas: Recognizing the challenge posed by textureless surfaces in neural implicit learning, HelixSurf introduces a scheme for regularization that capitalizes on image superpixel homogeneity. This aids in maintaining surface smoothness in regions lacking detailed texture information.
4. Use of Dynamic Space Occupancies: By maintaining dynamic occupancies within the 3D space, HelixSurf optimizes point sampling processes along rays during volume rendering. This novel technique leverages evolving occupancy grids to skip sampling in empty spaces, thereby enhancing rendering efficiency.

Experimental Validation and Results

The effectiveness of HelixSurf was validated through rigorous experimentation on benchmark datasets such as ScanNet and Tanks and Temples. Results consistently demonstrated that HelixSurf outperforms both traditional and state-of-the-art learned surface reconstruction techniques, achieving superior accuracy, completeness, and efficiency. Remarkably, it conducted training in significantly lesser time, even when compared against methods leveraging auxiliary data.

Notably, HelixSurf was shown to excel in scenarios involving complex scenes and textureless surfaces where other methods typically fail, providing high-quality detailed reconstructions and filling a critical gap in practical indoor scene reconstruction tasks.

Implications for Future Research

The methodologies introduced by HelixSurf open several avenues for further exploration and enhancements. Future work could focus on extending the approach to handle outdoor scenes with varying lighting conditions and complex textures. Additionally, the efficient volume rendering approach proposed could be adapted or expanded to other domains requiring speed and precision, including augmented reality and robotics.

Conclusion

HelixSurf advances the field of computer vision, particularly in 3D surface reconstruction from multi-view imagery, by bridging the gap between traditional and neural implicit methods through its intertwined regularization approach. It represents a significant step forward in both accuracy and efficiency of scene reconstruction methods, demonstrating practical applicability and setting a new benchmark for future research in the domain.