Robust Two-View Geometry Estimation with Implicit Differentiation

• URL: http://arxiv.org/abs/2410.17983v1
• Date: Wed, 23 Oct 2024 15:51:33 GMT
• Title: Robust Two-View Geometry Estimation with Implicit Differentiation
• Authors: Vladislav Pyatov, Iaroslav Koshelev, Stamatis Lefkimmiatis,
• Abstract summary: We present a novel two-view geometry estimation framework. It is based on a differentiable robust loss function fitting. We evaluate our approach on the camera pose estimation task in both outdoor and indoor scenarios.
• Score: 2.048226951354646
• License:
• Abstract: We present a novel two-view geometry estimation framework which is based on a differentiable robust loss function fitting. We propose to treat the robust fundamental matrix estimation as an implicit layer, which allows us to avoid backpropagation through time and significantly improves the numerical stability. To take full advantage of the information from the feature matching stage we incorporate learnable weights that depend on the matching confidences. In this way our solution brings together feature extraction, matching and two-view geometry estimation in a unified end-to-end trainable pipeline. We evaluate our approach on the camera pose estimation task in both outdoor and indoor scenarios. The experiments on several datasets show that the proposed method outperforms both classic and learning-based state-of-the-art methods by a large margin. The project webpage is available at: https://github.com/VladPyatov/ihls

Related papers

• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction [40.73187749820041]
  Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. A prevalent approach in current deep learning is the set-based approach which measures the discrepancy between two surfaces by comparing two randomly sampled point-clouds from the two meshes with Chamfer pseudo-distance. We propose a novel metric for learning mesh deformation, defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach.
  arXiv  Detail & Related papers  (2023-05-27T19:10:19Z)
• 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)
• Explicit Correspondence Matching for Generalizable Neural Radiance Fields [49.49773108695526]
  We present a new NeRF method that is able to generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The explicit correspondence matching is quantified with the cosine similarity between image features sampled at the 2D projections of a 3D point on different views. Our method achieves state-of-the-art results on different evaluation settings, with the experiments showing a strong correlation between our learned cosine feature similarity and volume density.
  arXiv  Detail & Related papers  (2023-04-24T17:46:01Z)
• Deep Active Ensemble Sampling For Image Classification [8.31483061185317]
  Active learning frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination of uncertainty-based and geometric approaches. We present an innovative integration of recent progress in both uncertainty-based and geometric frameworks to enable an efficient exploration/exploitation trade-off in sample selection strategy. Our framework provides two advantages: (1) accurate posterior estimation, and (2) tune-able trade-off between computational overhead and higher accuracy.
  arXiv  Detail & Related papers  (2022-10-11T20:20:20Z)
• Semantic keypoint-based pose estimation from single RGB frames [64.80395521735463]
  We present an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. We show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios.
  arXiv  Detail & Related papers  (2022-04-12T15:03:51Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• Learning Accurate Dense Correspondences and When to Trust Them [161.76275845530964]
  We aim to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. Our approach obtains state-of-the-art results on challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-01-05T18:54:11Z)
• Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints [80.60538408386016]
  Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry. We propose an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection.
  arXiv  Detail & Related papers  (2020-07-29T21:41:31Z)
• Indoor Layout Estimation by 2D LiDAR and Camera Fusion [3.2387553628943535]
  This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets. In the proposed system, a movable platform collects both intensity images and 2D LiDAR information.
  arXiv  Detail & Related papers  (2020-01-15T16:43:35Z)

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.