End2End Multi-View Feature Matching with Differentiable Pose Optimization

• URL: http://arxiv.org/abs/2205.01694v3
• Date: Mon, 11 Sep 2023 10:06:19 GMT
• Title: End2End Multi-View Feature Matching with Differentiable Pose Optimization
• Authors: Barbara Roessle and Matthias Nie{\ss}ner
• Abstract summary: We propose a graph attention network to predict image correspondences along with confidence weights. The resulting matches serve as weighted constraints in a differentiable pose estimation. We integrate information from multiple views by spanning the graph across multiple frames to predict the matches all at once.
• Score: 2.311583680973075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Erroneous feature matches have severe impact on subsequent camera pose estimation and often require additional, time-costly measures, like RANSAC, for outlier rejection. Our method tackles this challenge by addressing feature matching and pose optimization jointly. To this end, we propose a graph attention network to predict image correspondences along with confidence weights. The resulting matches serve as weighted constraints in a differentiable pose estimation. Training feature matching with gradients from pose optimization naturally learns to down-weight outliers and boosts pose estimation on image pairs compared to SuperGlue by 6.7% on ScanNet. At the same time, it reduces the pose estimation time by over 50% and renders RANSAC iterations unnecessary. Moreover, we integrate information from multiple views by spanning the graph across multiple frames to predict the matches all at once. Multi-view matching combined with end-to-end training improves the pose estimation metrics on Matterport3D by 18.5% compared to SuperGlue.

Related papers

• PRISM: PRogressive dependency maxImization for Scale-invariant image Matching [4.9521269535586185]
  We propose PRogressive dependency maxImization for Scale-invariant image Matching (PRISM) Our method's superior matching performance and generalization capability are confirmed by leading accuracy across various evaluation benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2024-08-07T07:35:17Z)
• DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses [59.51874686414509]
  Current approaches approximate the continuous pose representation with a large number of discrete pose hypotheses. We present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. Our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-20T15:41:32Z)
• PRAGO: Differentiable Multi-View Pose Optimization From Objectness Detections [19.211193336526346]
  We propose a Pose-refined Rotation Averaging Graph Optimization (PRAGO) method for differentiable estimating camera poses from a set of images. Our method reconstructs the rotational pose, and in turn, the absolute pose, in a differentiable manner benefiting from the optimization of a sequence of geometrical tasks. We show that PRAGO is able to outperform non-differentiable solvers on small and sparse scenes extracted from 7-Scenes achieving a relative improvement of 21% for rotations while achieving similar translation estimates.
  arXiv  Detail & Related papers  (2024-03-13T14:42:55Z)
• AffineGlue: Joint Matching and Robust Estimation [74.04609046690913]
  We propose AffineGlue, a method for joint two-view feature matching and robust estimation. AffineGlue selects potential matches from one-to-many correspondences to estimate minimal models. Guided matching is then used to find matches consistent with the model, suffering less from the ambiguities of one-to-one matches.
  arXiv  Detail & Related papers  (2023-07-28T08:05:36Z)
• IMP: Iterative Matching and Pose Estimation with Adaptive Pooling [34.36397639248686]
  We propose an textbfefficient IMP, called EIMP, to dynamically discard keypoints without potential matches. Experiments on YFCC100m, Scannet, and Aachen Day-Night datasets demonstrate that the proposed method outperforms previous approaches in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-04-28T13:25:50Z)
• PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching [51.142988196855484]
  We propose PoseMatcher, an accurate model free one-shot object pose estimator. We create a new training pipeline for object to image matching based on a three-view system. To enable PoseMatcher to attend to distinct input modalities, an image and a pointcloud, we introduce IO-Layer.
  arXiv  Detail & Related papers  (2023-04-03T21:14:59Z)
• Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking [98.91894395941766]
  We propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame. Specifically, we derive this prediction of dynamics through a graph neural network(GNN) that explicitly accounts for both spatial-temporal and visual information. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
  arXiv  Detail & Related papers  (2021-06-07T16:36:50Z)
• AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild [77.43884383743872]
  We present AdaFuse, an adaptive multiview fusion method to enhance the features in occluded views. We extensively evaluate the approach on three public datasets including Human3.6M, Total Capture and CMU Panoptic. We also create a large scale synthetic dataset Occlusion-Person, which allows us to perform numerical evaluation on the occluded joints.
  arXiv  Detail & Related papers  (2020-10-26T03:19:46Z)
• Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos [32.43899916477434]
  We propose an approach that relies on keypoint correspondences for associating persons in videos. Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image datasets for human pose estimation. Our approach achieves state-of-the-art results for multi-frame pose estimation and multi-person pose tracking on the PosTrack $2017$ and PoseTrack $2018$ data sets.
  arXiv  Detail & Related papers  (2020-04-27T09:02:24Z)
• Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer [116.46533207849619]
  We study the impact of scale and location mismatch in the few-shot learning scenario. We propose a novel Spatially-aware Matching scheme to effectively perform matching across multiple scales and locations.
  arXiv  Detail & Related papers  (2020-01-06T14:10:20Z)

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.