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PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images

CVPR 2022 · 3D vision › Depth and stereo · approach: Neural light field and illumination imaging · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the more complete SVBRDF representation and physics-based rendererPhyIR · FutureHouse test setre-render MSE: 0.0108 -> 0.0061Helped
Add circular padding (ablation)PhyIR material estimation · FutureHousebase color MSE: 0.955 -> 0.940Helped
Add circular padding (ablation)PhyIR material estimation · FutureHouseroughness MSE: 2.037 -> 1.934Helped
Jointly train the full pipeline (ablation)PhyIR material estimation · FutureHousebase color MSE: 0.940 -> 0.926Helped
Add the trainable guided filter module GSNet (ablation)PhyIR material estimation · FutureHousebase color MSE: 0.926 -> 0.902Helped
Add the spatial-coherence loss (ablation)PhyIR lighting estimation · FutureHouseSSIM: 0.6150 -> 0.6169Helped
Add the spatial-coherence loss (ablation)PhyIR lighting estimation · FutureHousere-render error (MSE): 0.0583 -> 0.0714Hurt
Add the physics-based re-render loss (ablation)PhyIR lighting estimation · FutureHousere-render error (MSE): 0.0714 -> 0.0060Helped
Jointly train the full pipeline (ablation)PhyIR lighting estimation · FutureHouseSSIM: 0.6124 -> 0.6169Helped
Jointly train the full pipeline (ablation)PhyIR lighting estimation · FutureHousere-render error (MSE): 0.0060 -> 0.0061Hurt
Use PhyIR instead of InvIndoorpanoramic lighting estimation · Real panoramic lighting datasetmirror silver sphere relighting MAE: 0.2407 -> 0.1117Helped
Use PhyIR instead of UniFusedepth estimation · 3D60RMSE: 0.1968 -> 0.2367Hurt

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