PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects

TL;DR

PISR is a novel polarimetric neural implicit surface reconstruction method that improves accuracy and speed, especially for textureless and specular objects, by leveraging polarization data and a specialized loss function.

Contribution

The paper introduces a geometrically accurate polarimetric loss and a hash-grid-based neural signed distance function for faster, more robust surface reconstruction from polarization images.

Findings

Achieves 0.5 mm L1 Chamfer distance

F-score of 99.5% at 1 mm

Converges 4 to 30 times faster than previous methods

Abstract

Neural implicit surface reconstruction has achieved remarkable progress recently. Despite resorting to complex radiance modeling, state-of-the-art methods still struggle with textureless and specular surfaces. Different from RGB images, polarization images can provide direct constraints on the azimuth angles of the surface normals. In this paper, we present PISR, a novel method that utilizes a geometrically accurate polarimetric loss to refine shape independently of appearance. In addition, PISR smooths surface normals in image space to eliminate severe shape distortions and leverages the hash-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 Chamfer distance of 0.5 mm and an F-score of 99.5% at 1 mm, while converging 4~30 times faster than previous polarimetric surface reconstruction methods.