EpiS improves generalizable neural surface reconstruction from sparse views by guiding epipolar feature aggregation with cost volumes, using an epipolar transformer, and applying pretrained monocular depth constraints, outperforming prior methods on DTU and BlendedMVS.
In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion
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Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry
EpiS improves generalizable neural surface reconstruction from sparse views by guiding epipolar feature aggregation with cost volumes, using an epipolar transformer, and applying pretrained monocular depth constraints, outperforming prior methods on DTU and BlendedMVS.