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We are witnessing an explosion of neural implicit representations in computervision and graphics. Their applicability has recently expanded beyond taskssuch as shape generation and image-based rendering to the fundamental problemof image-based 3D reconstruction. However, existing methods typically assumeconstrained 3D environments with constant illumination captured by a small setof roughly uniformly distributed cameras. We introduce a new method thatenables efficient and accurate surface reconstruction from Internet photocollections in the presence of varying illumination. To achieve this, wepropose a hybrid voxel- and surface-guided sampling technique that allows formore efficient ray sampling around surfaces and leads to significantimprovements in reconstruction quality. Further, we present a new benchmark andprotocol for evaluating reconstruction performance on such in-the-wild scenes.We perform extensive experiments, demonstrating that our approach surpassesboth classical and neural reconstruction methods on a wide variety of metrics.

Neural 3D Reconstruction in the Wild