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About this paper
Jiayu Yang, Wei Mao, Jose M. Alvarez, Miaomiao Liu
CVP-MVSNet introduces a neural network for multi-view stereo depth inference that leverages a cost volume pyramid to achieve state-of-the-art 3D reconstruction accuracy. The method is up to 6 times faster and utilizes 6 times less GPU memory than prior approaches, enabling efficient high-resolution depth map generation.
Key insights
- A coarse-to-fine strategy utilizing a cost volume pyramid significantly enhances computational efficiency (speed and memory) in MVS without compromising reconstruction accuracy.
- Principled depth sampling, based on a sub-pixel distance (e.g., 0.5 pixel) interval, optimizes the balance between information gain and computational load for cost volume construction.
- Iterative refinement of pixel-wise depth residuals through partial cost volumes allows for effective and efficient high-resolution depth map inference.
Summary by alphaXiv.
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