English(EN) Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion

新的深度学习框架提高了运动恢复结构的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新颖的深度学习框架用于运动恢复结构（SfM），该框架利用了一个置换等变、边条件图神经网络。该方法接收嘈杂的成对相对相机姿态，并输出全局一致的相机外参，而无需地面真实监督，而是依赖于相对姿态一致性目标。该框架效率高，可扩展到一千多张图像，并且在准确性和图像配准方面优于现有的深度跟踪中心方法，同时比最先进的经典流水线更快。 AI

影响这种新的深度学习方法可以通过提供更快、更准确的相机姿态估计，显著改进3D重建和视图合成流水线。

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arXiv cs.CV TIER_1 English(EN) · Fadi Khatib, Meirav Galun, Ronen Basri · 2026-09-10 04:00

从带噪声的视图图学习全局相机姿态以进行运动恢复结构

arXiv:2609.09491v1 Announce Type: new Abstract: Camera pose estimation is a key step in 3D reconstruction and view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation. Our method employs a permutation-equivariant,…

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从带噪声的视图图学习全局相机姿态以进行运动恢复结构

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