室内场景的深度全景深度预测和完成

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Giovanni Pintore, Eva Almansa, Armando Sanchez, Giorgio Vassena, Enrico Gobbetti
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引用次数: 0

摘要

我们介绍了一种新颖的端到端深度学习解决方案,用于快速估计室内环境的密集球形深度图。我们的输入是由各种常见捕捉设置提供的单个等角图像与稀疏深度图。深度由一个高效、轻便的单分支网络推断,该网络采用动态门控系统来处理密集的视觉数据和稀疏的几何数据。我们利用典型人造环境的特点来有效压缩多分辨率特征,并找到场景各部分之间的短距离和长距离关系。此外,我们还引入了一种新的增强策略,使模型对不同类型的稀疏性具有鲁棒性,包括由各种结构光传感器和激光雷达设置产生的稀疏性。实验结果表明,我们的方法具有交互性能,在计算效率、对不同深度稀疏模式的适应性以及对具有挑战性的室内数据的预测准确性方面都优于最先进的解决方案,即使仅在合成数据上进行训练而不做任何微调也是如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep panoramic depth prediction and completion for indoor scenes

Deep panoramic depth prediction and completion for indoor scenes

We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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