面向微运动视频的三维重建
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国网江苏省电力有限公司科技项目(J2020134)


3D Reconstruction for Small Motion Clips
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    摘要:

    手持相机拍照瞬间, 通常手部抖动可产生画面的微小运动. 一方面微小运动蕴含了视差信息, 将有助于进行场景深度感知并可潜在应用于虚拟/增强现实和照片重定焦等领域. 另一方面, 由于极窄的基线, 图像对应点匹配过程中对噪声较为敏感, 因而从无标定的微运动视频重建场景极具挑战性. 当前处理微运动视频三维重建的主流方法由于没有考虑重建过程的不确定性, 导致算法精度较差. 本文提出一种高精度的从无标定微运动视频复原场景深度的算法, 主要包含2个关键步骤: 首先, 在自标定阶段, 提出一种视点加权的光束平差方法, 充分考虑邻域视点间由于基线不同所产生的匹配不确定性, 减少较窄基线视点的可信度, 保持自标定过程的鲁棒性; 进一步地, 提出一种基于广义全变分平滑的深度图估计方法, 抑制窄基线产生的深度图噪声的同时保持倾斜结构和精细几何特征. 本文提出的方法与当前处理微运动三维重建的主流方法在真实和合成数据集上进行了定量和定性实验, 充分验证了提出方法的有效性.

    Abstract:

    When a user takes a photo, a small motion of image frames is usually induced by hand shaking. On the one hand, the small motion contains parallax information, which is valuable for scene depth perception and can be potentially used in many applications, such as VR/AR and photo refocusing. On the other hand, due to narrow baselines, corresponding point matching of images is sensitive to noise, as a result of which scene reconstruction from uncalibrated small motion clips is quite challenging. Existing state-of-the-art methods for 3D reconstruction from small motion clips are generally less accurate since they do not consider the uncertainties. In this study, we propose a high-accuracy method for 3D reconstruction from uncalibrated small motion clips. The proposed method consists of two key steps. Firstly, in the self-calibration stage, we propose a viewpoint-weighted bundle adjustment method that fully considers the matching uncertainties of different neighboring viewpoints due to different baselines and assigns smaller confidence to the viewpoints with narrower baselines, thereby keeping the robustness during self-calibration. Furthermore, we present a TGV-based depth image estimation method that can alleviate noise caused by narrow baselines while maintaining slanted structures and detailed geometric features. The quantitative and qualitative experiments on public datasets and synthetic datasets clearly demonstrate the effectiveness of the proposed method in comparison with state-of-the-arts.

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王晨麟,赵正,张涛,刘洋.面向微运动视频的三维重建.计算机系统应用,2022,31(7):298-306

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  • 收稿日期:2021-10-17
  • 最后修改日期:2021-11-17
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  • 在线发布日期: 2022-05-31
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