Stepwise Regression and Pre-trained Edge for Robust Stereo Matching

• URL: http://arxiv.org/abs/2406.06953v3
• Date: Sun, 16 Jun 2024 13:20:56 GMT
• Title: Stepwise Regression and Pre-trained Edge for Robust Stereo Matching
• Authors: Weiqing Xiao, Wei Zhao,
• Abstract summary: We propose a novel stereo matching method, called SR-Stereo, which mitigates the distributional differences across different datasets. We also propose Domain Adaptation Based on Pre-trained Edges (DAPE) to mitigate the edge blurring of the fine-tuned model on sparse ground truth. These proposed methods are extensively evaluated on SceneFlow, KITTI, Middbury 2014 and ETH3D.
• Score: 2.8908326904081334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the difficulty in obtaining real samples and ground truth, the generalization performance and the fine-tuned performance are critical for the feasibility of stereo matching methods in real-world applications. However, the presence of substantial disparity distributions and density variations across different datasets presents significant challenges for the generalization and fine-tuning of the model. In this paper, we propose a novel stereo matching method, called SR-Stereo, which mitigates the distributional differences across different datasets by predicting the disparity clips and uses a loss weight related to the regression target scale to improve the accuracy of the disparity clips. Moreover, this stepwise regression architecture can be easily extended to existing iteration-based methods to improve the performance without changing the structure. In addition, to mitigate the edge blurring of the fine-tuned model on sparse ground truth, we propose Domain Adaptation Based on Pre-trained Edges (DAPE). Specifically, we use the predicted disparity and RGB image to estimate the edge map of the target domain image. The edge map is filtered to generate edge map background pseudo-labels, which together with the sparse ground truth disparity on the target domain are used as a supervision to jointly fine-tune the pre-trained stereo matching model. These proposed methods are extensively evaluated on SceneFlow, KITTI, Middbury 2014 and ETH3D. The SR-Stereo achieves competitive disparity estimation performance and state-of-the-art cross-domain generalisation performance. Meanwhile, the proposed DAPE significantly improves the disparity estimation performance of fine-tuned models, especially in the textureless and detail regions.

Related papers

• Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data [3.1951121258423334]
  We develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes. We also develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes.
  arXiv  Detail & Related papers  (2024-11-07T13:53:13Z)
• Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching [77.133400999703]
  Correlation based stereo matching has achieved outstanding performance. Current methods with a fixed model do not work uniformly well across various datasets. This paper proposes a new perspective to dynamically calculate correlation for robust stereo matching.
  arXiv  Detail & Related papers  (2023-07-26T09:47:37Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer [60.31021888394358]
  Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) We propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data.
  arXiv  Detail & Related papers  (2023-03-31T03:14:44Z)
• Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation [42.16718847243166]
  Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an important class of solutions to this problem. This paper revisits the use of subspace alignment for UDA and proposes a novel adaptation algorithm that consistently leads to improved generalization.
  arXiv  Detail & Related papers  (2022-01-05T20:16:38Z)
• AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach [50.855679274530615]
  We present a novel domain-adaptive approach called AdaStereo to align multi-level representations for deep stereo matching networks. Our models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo. Our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
  arXiv  Detail & Related papers  (2021-12-09T15:10:47Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching [50.06646151004375]
  A novel domain-adaptive pipeline called AdaStereo aims to align multi-level representations for deep stereo matching networks. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo.
  arXiv  Detail & Related papers  (2020-04-09T16:15:13Z)

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.