Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

• URL: http://arxiv.org/abs/2104.05374v1
• Date: Mon, 12 Apr 2021 11:48:54 GMT
• Title: Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
• Authors: Hongbin Xu, Zhipeng Zhou, Yu Qiao, Wenxiong Kang, Qiuxia Wu
• Abstract summary: We propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods.
• Score: 39.95831985522991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method.

Related papers

• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• Disentangling Multi-view Representations Beyond Inductive Bias [32.15900989696017]
  We propose a novel multi-view representation disentangling method that ensures both interpretability and generalizability of the resulting representations. Our experiments on four multi-view datasets demonstrate that our proposed method outperforms 12 comparison methods in terms of clustering and classification performance.
  arXiv  Detail & Related papers  (2023-08-03T09:09:28Z)
• Self-Supervised Graph Transformer for Deepfake Detection [1.8133635752982105]
  Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset. Deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. This study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability.
  arXiv  Detail & Related papers  (2023-07-27T17:22:41Z)
• Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection [33.210575826086654]
  We present a weakly-supervised RGB-D salient object detection model via supervision. We focus on effective multimodal representation learning via inter-modal mutual information regularization.
  arXiv  Detail & Related papers  (2023-06-06T12:36:57Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id [109.1730454118532]
  Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. We present the hybrid dynamic cluster contrast and probability distillation algorithm. It formulates the unsupervised Re-Id problem into an unified local-to-global dynamic contrastive learning and self-supervised probability distillation framework.
  arXiv  Detail & Related papers  (2021-09-29T02:56:45Z)
• Learning Multimodal VAEs through Mutual Supervision [72.77685889312889]
  MEME combines information between modalities implicitly through mutual supervision. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes.
  arXiv  Detail & Related papers  (2021-06-23T17:54:35Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)
• Joint Generative and Contrastive Learning for Unsupervised Person Re-identification [15.486689594217273]
  Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework.
  arXiv  Detail & Related papers  (2020-12-16T16:49:57Z)

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.