用偏振信息提升无纹理和反光物体的三维重建精度与速度
PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects
- 基于偏振图像的几何约束优化表面形状，与外观解耦
- 重建误差低至0.5毫米，1毫米阈值下准确率99.5%
- 采用哈希网格加速，比之前方法快4~30倍
神经隐式表面重建近期取得显著进展，但现有方法在无纹理和镜面表面仍表现不佳。与RGB图像不同，偏振图像能直接提供表面法向方位角的约束。本文提出PISR，一种利用几何精确偏振损失独立优化形状的方法。PISR在图像空间平滑表面法向以消除严重形变，并采用基于哈希网格的神经有符号距离函数加速重建过程。实验表明，PISR在保持高精度的同时显著提升收敛速度：在1毫米阈值下，L1 Chamfer距离达0.5毫米，F-score为99.5%，相比先前极化表面重建方法收敛速度快4~30倍。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。