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Deep learning has made significant impacts on multi-view stereo systems.State-of-the-art approaches typically involve building a cost volume, followedby multiple 3D convolution operations to recover the input image's pixel-wisedepth. While such end-to-end learning of plane-sweeping stereo advances publicbenchmarks' accuracy, they are typically very slow to compute. We present\ouralg, a highly efficient multi-view stereo algorithm that seamlesslyintegrates multi-view constraints into single-view networks via an attentionmechanism. Since \ouralg only builds on 2D convolutions, it is at least$2\times$ faster than all the notable counterparts. Moreover, our algorithmproduces precise depth estimations and 3D reconstructions, achievingstate-of-the-art results on challenging benchmarks ScanNet, SUN3D, RGBD, andthe classical DTU dataset. our algorithm also out-performs all other algorithmsin the setting of inexact camera poses. Our code is released at\url{https://github.com/zhenpeiyang/MVS2D}

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