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Multi-scale Edge-guided Learning for 3D Reconstruction

Single-view 3D object reconstruction has always been a long-term challenging task. The objects with complex topologies are hard to accurately be reconstructed, which makes the existing methods suffer from the blurring of the shape boundary between the multiple components in the object. Moreover, most of them cannot balance the learning between global geometric structure information and local details information well. In this paper, 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, which aims to exploit the multi-scale learning strategy to learn global edge information and local details, thus achieving robust 3D object reconstruction. Specifically, we first design a multi-scale Gaussians 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. Secondly, we design a multi-scale feature interaction block (MFIB) for the learning of 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 to state-of-the-art methods. Code is available at https://github.com/Ray-tju/MEGLN.