AutoRecon：自动化3D物体发现和重建

摘要

数字内容创作中，完全自动化的物体重建流程至关重要。虽然3D重建领域取得了深刻的发展，但为了获得干净的物体模型，仍然依赖不同形式的手动劳动，如边界框标注、蒙版注释和网格操作来移除背景。本文提出了一个名为AutoRecon的新颖框架，用于自动发现和重建多视角图像中的物体。我们展示了通过利用自监督的2D视觉Transformer特征，可以从SfM点云中强大地定位和分割前景物体。然后，我们通过分解点云提供的密集监督，重建分解的神经场景表示，从而实现准确的物体重建和分割。在DTU、BlendedMVS和CO3D-V2数据集上的实验表明了AutoRecon的有效性和稳健性。

English

A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2D vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon.

AutoRecon：自动化3D物体发现和重建

AutoRecon: Automated 3D Object Discovery and Reconstruction

摘要

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