  Neural implicit surfaces have become an important technique for multi-view 3D
reconstruction but their accuracy remains limited. In this paper, we argue that
this comes from the difficulty to learn and render high frequency textures with
neural networks. We thus propose to add to the standard neural rendering
optimization a direct photo-consistency term across the different views.
Intuitively, we optimize the implicit geometry so that it warps views on each
other in a consistent way. We demonstrate that two elements are key to the
success of such an approach: (i) warping entire patches, using the predicted
occupancy and normals of the 3D points along each ray, and measuring their
similarity with a robust structural similarity (SSIM); (ii) handling visibility
and occlusion in such a way that incorrect warps are not given too much
importance while encouraging a reconstruction as complete as possible. We
evaluate our approach, dubbed NeuralWarp, on the standard DTU and EPFL
benchmarks and show it outperforms state of the art unsupervised implicit
surfaces reconstructions by over 20% on both datasets.
