Beschreibung:
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Single-view three-dimensional (3D) object reconstruction has always been a long-term challenging task. Objects with complex topologies are hard to accurately reconstruct, which makes existing methods suffer from blurring of shape boundaries between multiple components in the object. Moreover, most of them cannot balance learning between global geometric structure information and local detail information. In this article, we propose a multi-scale edge-guided learning network (MEGLN) to utilize the global edge information guiding the network to better capture and recover local details. The goal is to exploit the multi-scale learning strategy to learn global edge information and local details, thus achieving robust 3D object reconstruction. We first design a multi-scale Gaussian difference block (MGDB) to extract global edge geometry features for input images of different scales and adopt the attention mechanism to aggregate the extracted global edge geometry features of different scales. Second, we design a multi-scale feature interaction block (MFIB) to learn local details, which utilizes the multi-scale feature interaction to capture the features of multiple objects or components at multiple scales. The MFIB can learn and capture better as much local detail information as possible under the guidance of global edge information. Finally, we dynamically fuse the predicted probabilities of the MGDB and MFIB to obtain the final predicted result, which makes our MEGLN able to recover 3D shapes with global complex topological structures and rich local details via the multi-scale learning strategy. Extensive qualitative and quantitative experimental results on the ShapeNet dataset demonstrate that our approach achieves competitive performance compared with state-of-the-art methods. Code is available at
<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="url" xlink:href="https://github.com/Ray-tju/MEGLN">https://github.com/Ray-tju/MEGLN</jats:ext-link>
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