MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions

Understand

Deep learning has made significant impacts on multi-view stereo systems.

• State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth.
• While such end-to-end learning of plane-sweeping stereo advances public benchmarks' accuracy, they are typically very slow to compute.
• We present \ouralg, a highly efficient multi-view stereo algorithm that seamlessly integrates multi-view constraints into single-view networks via an attention mechanism.