Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement (2003.13017v1)

Abstract: Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we first construct a sparse cost volume for learning a sparse and high-resolution depth map. Then we leverage a small-scale convolutional neural network to encode the depth dependencies for pixels within a local region to densify the sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton layer is proposed to further optimize the depth map. On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method. On the other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee the efficiency of our approach. Besides, our approach is also memory-friendly because of the sparse depth representation. Extensive experimental results show that our method is 5$\times$ and 14$\times$ faster than Point-MVSNet and R-MVSNet, respectively, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset. Code is available at https://github.com/svip-lab/FastMVSNet.

• The paper introduces a sparse-to-dense MVS framework that leverages sparse cost volume construction and learned depth propagation for efficient 3D reconstruction.
• It demonstrates significant speed gains by achieving up to 5× and 14× faster performance than Point-MVSNet and R-MVSNet while maintaining high reconstruction quality.
• The integration of a Gauss-Newton refinement layer in an end-to-end learnable module optimizes depth predictions, making it suitable for real-time computer vision applications.

Overview of Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement

The paper "Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement" presents an innovative approach to enhancing both the speed and accuracy of multi-view stereo (MVS) methods, which are fundamental in computer vision for reconstructing dense 3D structures from multiple images. In contrast to many prior approaches that predominantly prioritize reconstruction quality, this work emphasizes the importance of efficiency in real-world applications.

Key Components

The Fast-MVSNet framework introduced in this paper is characterized by a sparse-to-dense, coarse-to-fine approach featuring three primary components:

1. Sparse Cost Volume Construction: The method begins with the construction of a sparse cost volume aimed at predicting a high-resolution depth map with sparse data points. This stage leverages 3D convolutional neural networks for volume regularization, distinguishing it from traditional, dense multi-scale 3D CNNs used in other deep learning-based MVS approaches, which are often computationally expensive and memory-intensive.
2. Learned Depth Propagation: A convolutional neural network (CNN) is employed to encode depth dependencies within local image regions to densify the initially sparse depth map. This propagation stage draws inspiration from techniques like joint bilateral upsampling but surpasses them by learning to adaptively propagate depth information based on data characteristics.
3. Gauss-Newton Refinement: The third component is a Gauss-Newton layer designed to optimize the depth map. This layer integrates Gauss-Newton optimization into a differentiable, end-to-end learnable module, allowing the network to refine depth predictions for enhanced accuracy.

Empirical Results

The experimental results presented in the paper underscore the efficiency and efficacy of Fast-MVSNet. The method is reported to be significantly faster—5$\times$ and 14$\times$—compared to Point-MVSNet and R-MVSNet, respectively, while achieving similar or superior results in terms of reconstruction quality on the Tanks and Temples dataset and the DTU dataset. Such improvements highlight the achievement of a critical balance between computational efficiency and reconstruction accuracy.

Implications and Speculations

From a practical standpoint, Fast-MVSNet represents a step forward in making high-quality 3D reconstructions feasible for real-time applications, such as augmented reality and autonomous systems, where both speed and accuracy are pivotal. The sparse-to-dense strategy proves to be not only efficient but also scalable, thus applicable to scenarios involving high-resolution imagery and extensive datasets.

Theoretically, the integration of optimization algorithms, like Gauss-Newton, within a neural network framework for the purpose of regularization and refinement suggests a promising direction for future research in hybrid models that amalgamate traditional optimization concepts with deep learning. Additionally, the data-driven propagation mechanism introduces an adaptable and trainable approach to depth refinement, which could be explored further to tackle diverse challenges in computer vision.

In conclusion, Fast-MVSNet offers an impactful contribution to the MVS domain by prioritizing efficiency without compromising quality and sets a foundation for further investigations into lightweight, adaptive MVS solutions that could be applied in complex, large-scale 3D reconstruction tasks. Future advancements may consider extending this framework to more generalized scenes and enhancing the learned components to foster greater robustness against varying image conditions and greater integration with other vision-based systems.