A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning-based multi-view stereo (MVS) algorithms have emerged, replacing conventional hand-crafted features with convolutional neural network-encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state-of-the-art architectures are not designed for an indoor environment with abundant weakly textured or textureless objects. This paper proposes AttentionSPP-PatchmatchNet, a deep learning-based MVS algorithm designed for indoor 3D reconstruction. The algorithm integrates multi-scale feature sampling to produce global-context-aware feature maps and recalibrates the weight of essential features to tackle challenges posed by indoor environments. A new dataset designed exclusively for indoor environments is presented to verify the performance of the proposed network. Experimental results show that AttentionSPP-PatchmatchNet outperforms state-of-the-art algorithms with relative 132.87% and 163.55% improvements at the 10 and 2 mm threshold, respectively, making it suitable for accurate and complete indoor 3D reconstruction.

中文翻译：

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用于弱纹理室内 3D 重建的多视图立体

3D 重建可实现有效的几何表示以支持各种应用。最近，基于学习的多视图立体（MVS）算法出现了，用卷积神经网络编码的深度表示取代了传统的手工制作的特征，以减少特征匹配的模糊性，从而从图像数据中实现更完整的场景恢复。然而，最先进的架构并不是为具有大量弱纹理或无纹理对象的室内环境而设计的。本文提出了 AttentionSPP-PatchmatchNet，这是一种基于深度学习的 MVS 算法，专为室内 3D 重建而设计。该算法集成了多尺度特征采样，以生成全局上下文感知特征图，并重新校准基本特征的权重，以应对室内环境带来的挑战。提出了专门为室内环境设计的新数据集来验证所提出的网络的性能。实验结果表明，AttentionSPP-PatchmatchNet 的性能优于最先进的算法，在 10 毫米和 2 毫米阈值下分别相对提高了 132.87% 和 163.55%，使其适合精确、完整的室内 3D 重建。