Problem
Research questions and friction points this paper is trying to address.
Camera Pose Estimation
Noisy View-Graphs
Structure from Motion
3D Reconstruction
Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion
🤖 AI Summary
本文提出一种基于学习的视图图聚合方法，通过图神经网络从噪声相对位姿估计全局一致的相机外参，用于三维重建。
📝 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, edge-conditioned graph neural network that takes noisy pairwise relative poses as input and outputs globally consistent camera extrinsics. The network is trained without ground-truth supervision, relying solely on a relative-pose consistency objective. This is followed by 3D point triangulation and robust bundle adjustment. Our approach is efficient, scalable to more than a thousand images, and robust to graph density. We evaluate our method on MegaDepth, 1DSfM, Strecha, and BlendedMVS. These experiments demonstrate that our method achieves superior rotation and translation accuracy compared to deep track-centric methods while registering more images across many scenes, and competitive results compared to state-of-the-art classical pipelines, while being much faster.
Innovation
Methods, ideas, or system contributions that make the work stand out.
permutation-equivariant
edge-conditioned graph neural network
global camera poses
noisy view-graphs
relative-pose consistency
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