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SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans

CVPR 2021 · 3D vision › 3D generation · approach: Generative indoor scene synthesis · 37 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use 2D view-guided rendering losses instead of 3D target supervision (ablation)SPSG · Matterport3DIoU: 0.33 -> 0.39Helped
use 2D view-guided rendering losses instead of 3D target supervision (ablation)SPSG · Matterport3DFID: 80.51 -> 56.03Helped
add adversarial and perceptual losses to the ℓ1 reconstruction loss (ablation)SPSG · Matterport3DFID: 67.92 -> 56.03Helped
add adversarial loss (ablation)SPSG · Matterport3DFID: 62.15 -> 56.03Helped
add perceptual loss (ablation)SPSG · Matterport3DFID: 61.46 -> 56.03Helped
use CIELAB instead of RGB color space (ablation)SPSG · Matterport3DFID: 58.8 -> 56.03Helped
use CIELAB instead of RGB color space (ablation)SPSG · Matterport3DFeature-ℓ1: 0.222 -> 0.219Helped
use 2D view-guided losses instead of a 3D ℓ1 loss only (ablation)SPSG · Matterport3DIoU: 0.31 -> 0.39Helped
train on real Matterport3D data instead of synthetic dataSG-NN · Matterport3DIoU: 0.27 -> 0.28Helped

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