Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion

• URL: http://arxiv.org/abs/2609.09491v1
• Date: Tue, 08 Sep 2026 22:14:03 GMT
• Title: Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion
• Abstract summary: We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation.<n>Our method employs a permutation-equivariant, edge-conditioned graph neural network that takes noisy pairwise relative poses.<n>We evaluate our method on MegaDepth, 1DSfM, Strecha, and BlendedMVS.
• Score: 12.851731302874171
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Geo-Align: Video Generation Alignment via Metric Geometry Reward [53.65904111864641]
  Geo-Align is the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering.<n>Built upon a pretrained model, we optimize the model through a scale-aware perceptual reward mechanism.<n>Experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity.
  arXiv  Detail & Related papers  (2026-05-22T17:59:43Z)
• Unblur-SLAM: Dense Neural SLAM for Blurry Inputs [68.94602568657164]
  Un-SLAM is a novel pipeline for sharp 3D reconstruction from blurred image inputs.<n>We demonstrate state-of-the-art performance in the presence of both motion blur and defocus blur.<n>Experiments on several real-world datasets demonstrate consistent improvements in both pose estimation and sharp reconstruction results.
  arXiv  Detail & Related papers  (2026-03-26T14:29:47Z)
• FaVoR: Features via Voxel Rendering for Camera Relocalization [23.7893950095252]
  Camera relocalization methods range from dense image alignment to direct camera pose regression from a query image.<n>We propose a novel approach that leverages a globally sparse yet locally dense 3D representation of 2D features.<n>By tracking and triangulating landmarks over a sequence of frames, we construct a sparse voxel map optimized to render image patch descriptors observed during tracking.
  arXiv  Detail & Related papers  (2024-09-11T18:58:16Z)
• Visual Geometry Grounded Deep Structure From Motion [20.203320509695306]
  We propose a new deep pipeline VGGSfM, where each component is fully differentiable and can be trained in an end-to-end manner. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
  arXiv  Detail & Related papers  (2023-12-07T18:59:52Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild [57.37891682117178]
  We present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence from pairwise optical flow. A novel neural network architecture is proposed for processing irregular point trajectory data. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories.
  arXiv  Detail & Related papers  (2022-07-19T09:19:45Z)
• FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction [37.81077373162092]
  Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. We present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
  arXiv  Detail & Related papers  (2022-05-16T15:39:27Z)
• 3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning [11.599633757222406]
  Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents.
  arXiv  Detail & Related papers  (2020-05-13T04:15:14Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.