We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image. Limited by the nature of deep neural network, previous methods usually represent a 3D shape in volume or point cloud, and it is non-trivial to convert them to the more ready-to-use mesh model. Unlike the existing methods, our network represents 3D mesh in a graph-based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image. We adopt a coarse-to-fine strategy to make the whole deformation procedure stable, and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.
Figure 1: We provide evidence that state-of-the-art single-view 3D reconstruction methods (AtlasNet (light green, 0.38 IoU) [12], OGN (green, 0.46 IoU) [46], Matryoshka Networks (dark green, 0.47 IoU) [37]) do not actually perform reconstruction but image classification. We explicitly design pure recognition baselines (Clustering (light blue, 0.46 IoU) and Retrieval (dark blue, 0.57 IoU)) and show that they produce similar or better results both qualitatively and quantitatively. For reference, we show the ground truth (white) and a nearest neighbor from the training set (red, 0.76 IoU). The inset shows the input image.
AbstractConvolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by the idea of having an encoder-decoder network that performs non-trivial reasoning about the 3D structure of the output space. In this work, we set up two alternative approaches that perform image classification and retrieval respectively. These simple baselines yield better results than state-of-the-art methods, both qualitatively and quantitatively. We show that encoder-decoder methods are statistically indistinguishable from these baselines, thus indicating that the current state of the art in single-view object reconstruction does not actually perform reconstruction but image classification. We identify aspects of popular experimental procedures that elicit this behavior and discuss ways to improve the current state of research.
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