EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images

• URL: http://arxiv.org/abs/2302.10544v2
• Date: Sun, 6 Aug 2023 09:51:02 GMT
• Title: EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images
• Authors: Zhichao Ye, Chong Bao, Xin Zhou, Haomin Liu, Hujun Bao, Guofeng Zhang
• Abstract summary: This paper presents an efficient covisibility-based incremental SfM for unordered Internet images. We propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. The proposed method is three times faster than the state of the art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy.
• Score: 24.6736600856999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state of the art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://github.com/openxrlab/xrsfm

Related papers

• Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment [0.7499722271664144]
  Contrastive Language and Image Pairing (CLIP) is a transformative method in multimedia retrieval. CLIP typically trains two neural networks concurrently to generate joint embeddings for text and image pairs. This paper addresses the challenge of optimizing CLIP models for various image-based similarity search scenarios.
  arXiv  Detail & Related papers  (2024-09-03T14:33:01Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Emergent Correspondence from Image Diffusion [56.29904609646015]
  We show that correspondence emerges in image diffusion models without any explicit supervision. We propose a strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT) DIFT is able to outperform both weakly-supervised methods and competitive off-the-shelf features in identifying semantic, geometric, and temporal correspondences.
  arXiv  Detail & Related papers  (2023-06-06T17:33:19Z)
• {\mu}Split: efficient image decomposition for microscopy data [50.794670705085835]
  muSplit is a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We introduce lateral contextualization (LC), a novel meta-architecture that enables the memory efficient incorporation of large image-context. We apply muSplit to five decomposition tasks, one on a synthetic dataset, four others derived from real microscopy data.
  arXiv  Detail & Related papers  (2022-11-23T11:26:24Z)
• Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image Translation [9.900050049833986]
  We propose EnCo, a simple but efficient way to maintain the content by constraining the representational similarity in the latent space of patch-level features. For the similarity function, we use a simple MSE loss instead of contrastive loss, which is currently widely used in I2I tasks. In addition, we rethink the role played by discriminators in sampling patches and propose a discnative attention-guided (DAG) patch sampling strategy to replace random sampling.
  arXiv  Detail & Related papers  (2022-11-20T04:39:57Z)
• Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering [15.056672221375104]
  Deep learning-based image compression has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC. Many leading learned schemes cannot maintain a good trade-off between performance and complexity. We propose an effcient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art.
  arXiv  Detail & Related papers  (2022-06-21T09:34:29Z)
• FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning [64.32306537419498]
  We propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. These transformations also use information from both within-class and across-class representations that we extract through clustering. We demonstrate that our method is comparable to current state of art for smaller datasets while being able to scale up to larger datasets.
  arXiv  Detail & Related papers  (2020-07-16T17:55:31Z)
• Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration [36.22821902478044]
  We propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step and produce equivalent results with much cheaper computation. Experimental results demonstrate that Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms.
  arXiv  Detail & Related papers  (2020-06-24T13:24:37Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

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.