Degradation-agnostic Correspondence from Resolution-asymmetric Stereo

• URL: http://arxiv.org/abs/2204.01429v1
• Date: Mon, 4 Apr 2022 12:24:34 GMT
• Title: Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
• Authors: Xihao Chen, Zhiwei Xiong, Zhen Cheng, Jiayong Peng, Yueyi Zhang, Zheng-Jun Zha
• Abstract summary: We study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. We propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. We find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
• Score: 96.03964515969652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty of obtaining ground-truth disparity labels in diverse real-world systems, we start from an unsupervised learning perspective. However, resolution asymmetry caused by unknown degradations between two views hinders the effectiveness of the generally assumed photometric consistency. To overcome this challenge, we propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features. These features can then be utilized to formulate a feature-metric loss to avoid the photometric inconsistency. Moreover, we introduce a self-boosting strategy to optimize the feature extractor progressively, which further strengthens the feature-metric consistency. Experiments on both simulated datasets with various degradations and a self-collected real-world dataset validate the superior performance of the proposed method over existing solutions.

Related papers

• Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo Matching [0.0]
  Self-supervised stereo matching holds great promise for application and research. Direct self-supervised stereo matching paradigms based on photometric loss functions have consistently struggled with performance issues. We propose a simple yet highly effective pseudo-stereo inputs strategy to address the core occlusion challenge.
  arXiv  Detail & Related papers  (2024-10-03T14:40:17Z)
• Learning Layer-wise Equivariances Automatically using Gradients [66.81218780702125]
  Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted. Our goal is to allow flexible symmetry constraints that can automatically be learned from data using gradients.
  arXiv  Detail & Related papers  (2023-10-09T20:22:43Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
  We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
  arXiv  Detail & Related papers  (2023-02-16T10:40:55Z)
• CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose [13.581694284209885]
  Photometric differences are used to train neural networks for estimating depth and camera pose from unlabeled monocular videos. In this paper, we deal with moving objects and occlusions utilizing the difference of the flow fields and depth structure generated by affine transformation and view synthesis. We mitigate the effect of textureless regions on model optimization by measuring differences between features with more semantic and contextual information without adding networks.
  arXiv  Detail & Related papers  (2022-12-12T12:18:24Z)
• Frequency-Aware Self-Supervised Monocular Depth Estimation [41.97188738587212]
  We present two versatile methods to enhance self-supervised monocular depth estimation models. The high generalizability of our methods is achieved by solving the fundamental and ubiquitous problems in photometric loss function. We are the first to propose blurring images to improve depth estimators with an interpretable analysis.
  arXiv  Detail & Related papers  (2022-10-11T14:30:26Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Feature-metric Loss for Self-supervised Learning of Depth and Egomotion [13.995413542601472]
  Photometric loss is widely used for self-supervised depth and egomotion estimation. In this work, feature-metric loss is proposed and defined on feature representation. Comprehensive experiments and detailed analysis via visualization demonstrate the effectiveness of the proposed feature-metric loss.
  arXiv  Detail & Related papers  (2020-07-21T05:19:07Z)
• Gated Fusion Network for Degraded Image Super Resolution [78.67168802945069]
  We propose a dual-branch convolutional neural network to extract base features and recovered features separately. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process.
  arXiv  Detail & Related papers  (2020-03-02T13:28:32Z)

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.