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NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

NeurIPS 2021 · 3D vision › Face and shape · approach: Neural implicit surface reconstruction · 85 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use NeuS unbiased opaque-density weight instead of naive volume-rendering weight w=T*sigma with S-density (ablation)NeuS · DTU (ablation scene)Chamfer distanceHelped
use NeuS weight instead of direct normalized S-density weight (Eqn 4) (ablation)NeuS · DTU (ablation scene)reconstruction qualityHelped
add Eikonal regularization (ablation)NeuS · DTU (ablation scene)Chamfer distanceNo clear effect
add geometric initialization (ablation)NeuS · DTU (ablation scene)Chamfer distanceNo clear effect
add Eikonal regularization and geometric initialization (ablation)NeuS · DTU (ablation scene)SDF MAEHelped
NeuS vs IDR, without mask supervision for NeuSNeuS · DTUmean Chamfer distance: 0.90 (IDR w/ mask) vs 0.84 (NeuS w/o mask)Mixed
replace surface rendering (IDR) with NeuS volume rendering, both with maskIDR · DTU w/ maskmean Chamfer distance: 0.90 -> 0.77Helped
replace NeRF density with NeuS SDF volume rendering, w/o maskNeRF · DTU w/o maskmean Chamfer distance: 1.49 -> 0.84Helped
replace UNISURF occupancy with NeuS SDF representation, w/o maskUNISURF · DTU w/o maskmean Chamfer distance: 1.02 -> 0.84Helped

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