Poppy: Polarization-Based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
Refines surface normals from frozen RGB backbones using polarization measurements. Input/output offsets and reflectance decomposition are optimized through differentiable rendering to add physical constraints at inference time.
Overview
Refines surface normals from frozen RGB backbones using polarization measurements. Input/output offsets and reflectance decomposition are optimized through differentiable rendering to add physical constraints at inference time.
Refines surface normals from frozen RGB backbones using polarization measurements. Input/output offsets and reflectance decomposition are optimized through differentiable rendering to add physical constraints at inference time.
When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.
Refines surface normals from frozen RGB backbones using polarization measurements. Input/output offsets and reflectance decomposition are optimized through differentiable rendering to add physical constraints at inference time.
Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.
References
ID: eccv-2026-poppy · Last verified:
