Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction (2208.12697v5)

Abstract: Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene. Recent efforts explore the explicit volumetric representation to accelerate the optimization via memorizing significant information with learnable voxel grids. However, existing voxel-based methods often struggle in reconstructing fine-grained geometry, even when combined with an SDF-based volume rendering scheme. We reveal that this is because 1) the voxel grids tend to break the color-geometry dependency that facilitates fine-geometry learning, and 2) the under-constrained voxel grids lack spatial coherence and are vulnerable to local minima. In this work, we present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels. Extensive experiments show that Voxurf achieves high efficiency and high quality at the same time. On the DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x training speedup compared to previous fully implicit methods. Our code is available at https://github.com/wutong16/Voxurf.

• The paper introduces a two-stage training process that first builds a coarse shape and then refines fine details for enhanced global structure capture.
• The paper presents a dual color network that leverages voxelized color information to preserve accurate color-geometry relationships during reconstruction.
• The paper achieves a 20x speedup on the DTU dataset by using hierarchical geometry features to improve spatial coherence and overall fidelity.

Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

The paper "Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction" presents a novel approach addressing the challenges of neural surface reconstruction using voxel grids. Focusing on achieving efficiency and accuracy, the authors identify limitations in existing voxel-based methods, such as difficulties in capturing fine-grained geometries and issues with spatial coherence.

Key Contributions

1. Two-Stage Training Procedure: Voxurf employs a two-stage training process. Initially, a coherent coarse shape is constructed, followed by a refinement stage that recovers fine geometric details. This approach enables the model to capture both global structure and intricate surface details efficiently.
2. Dual Color Network: To maintain the color-geometry dependency essential for high-fidelity geometry learning, Voxurf introduces a dual color network architecture. This design leverages voxeled color information while preserving the delicate relationship between surface normals and color cues, crucial for accurate detail reconstruction.
3. Hierarchical Geometry Features: The authors implement a hierarchical geometry feature to facilitate information propagation across voxels. This component enhances spatial coherence, mitigating local minima issues that often arise in voxel grid optimizations.
4. Implementation and Results: On the DTU dataset, Voxurf demonstrates a substantial 20x training speedup compared to fully implicit methods like NeuS, while achieving superior reconstruction fidelity. The numerical results underscore its efficacy, with significant improvements in Chamfer Distance and image rendering quality.

Implications and Future Directions

Practically, Voxurf's methodology significantly reduces the computation time for training on 3D scenes without compromising on quality. This efficiency can dramatically widen the application of neural surface reconstruction in various domains including augmented reality, 3D modeling, and robotics. Theoretically, the approach opens avenues for further research into hybrid architectures that can leverage both explicit and implicit neural representations more effectively.

Potential future work could explore the extension of Voxurf to handle dynamic scenes or integrate it with multi-sensor inputs for more robust performance in real-world applications. Additionally, investigating adaptions or enhancements to handle large-scale environments while maintaining its computational efficiency would be a valuable direction.

In conclusion, Voxurf represents a significant advancement in neural surface reconstruction, with potential implications for rapidly evolving AI-driven technologies that require quick, accurate 3D modeling.