English(EN) Surface Reconstruction from Range Scans Under Visibility Constraints

新研究解决了带可见性约束的表面重建问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新发表在arXiv上的研究论文详细介绍了一种从范围扫描中进行表面重建的新方法，特别解决了噪声和异常值带来的挑战。该方法将表面重建构建为一个鲁棒回归问题，利用了通常被其他神经技术丢弃的传感器位置和可见性信息。该方法将表面表示为1-Lipschitz多层感知器的零集，并采用Huber损失进行回归，在合成数据和真实世界数据上都展现出与最先进方法相当的性能。 AI

影响这项研究可能导致从传感器数据中进行更准确、更鲁棒的3D建模，影响机器人和增强现实等领域。

排序理由该集群包含一篇提交到arXiv的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Gabriel Lenain, Edmond Boyer, Jean Ponce · 2026-10-27 04:00

可见性约束下的深度扫描表面重建

arXiv:2610.18005v2 Announce Type: replace Abstract: Surface reconstruction from range scans is challenging in the presence of noise and outliers, as fidelity to observations must be balanced against sensitivity to measurement errors. Yet, most neural approaches discard the sensor…

本文如何被排名

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可见性约束下的深度扫描表面重建

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