![]() We will also discuss using the geometric nets of different 3-D solids to find their surface area.Ī geometric net can be defined as a two-dimensional shape that can be modified to form a three-dimensional shape or a solid.Ī net is defined as a pattern obtained when a three-dimensional figure is laid out flat, showing each face of the figure.What a geometric net is and a geometric net definition,.A given net may be folded into a different convex polyhedron, depending upon the angles in which the edges are folded and which edges are joined together. A polyhedron net is a shape where a non-overlapping edge joined polygons in the plane, re-arranged into another shape.Īlbrecht Durer talked about nets in the book he wrote in 1525, named “A Course in the Art of Measurement with Compass and Ruler.” The arrangement of edges decides the shapes of the nets. Qualitative results on Single Image 3D reconstruction on SMAL Dataset using DISN as the 3D reconstruction method and 3DStyleNet as a 3D Data Augmentation Strategy. Ours produces the most plausible and smooth shapes. No augmentation produces worst results than the remaining augmentation strategies. While none of the results are perfect, some are clearly worse than others. Qualitative results on Single Image 3D reconstruction using DISN as the 3D reconstruction method and various 3D Data Augmentation strategies. Quantitative results on Single Image 3D reconstruction using DISN as the 3D reconstruction method and 3DStyleNet compared with baselines as a 3D data augmentation strategy. The interpolation is fast and can be done in real-time.ģDStyleNet can be used as a 3D Data Augmentation tool for downstream computer vision tasks. Our method supports linear shape interpolation between the source shape and the stylized output:įor geometry, we linearly interpolate the vertex displacements produced by the part-ware affine transformation.įor the texture, we linearly interpolate the vgg features and decode them to get the interpolated texture images. While the baseline simply enlarges the dog's head, our method jointly stylizes both geometry and texture to achieve the cartoon look of the target object. See for example the 5th column of the animal subset. Notice that our method better captures the style in both geometry and texture. a strong baseline that combines NeuralCage + Linear Image Style Transfer. Extensive quantitative analysis shows that 3DStyleNet outperforms alternative data augmentation techniques for the downstream task of single-image 3D reconstruction. In addition, our method can serve as a valuable tool to create 3D data augmentations for computer vision tasks. ![]() We showcase our approach qualitatively on 3D content stylization, and provide user studies to validate the quality of our results. Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation. Second, we jointly optimize our geometric style network and a pre-trained image style transfer network with losses defined over both the geometry and the rendering of the result. First, the geometric style network is trained on a large set of untextured 3D shapes. Our model, 3DStyleNet, is composed of two sub-networks trained in two stages. In addition, the texture style of the target is transferred to the warped source object with the help of a multi-view differentiable renderer. Given a pair of textured source and target objects, our method predicts a part-aware affine transformation field that naturally warps the source shape to imitate the overall geometric style of the target. We propose a method to create plausible geometric and texture style variations of 3D objects in the quest to democratize 3D content creation. ![]() Your browser does not support the video tag. Our method creates novel geometric and texture variations of 3D objects by transferring the shape and texture style from one 3D object (target) to another (source). We propose 3DStyleNet, a neural stylization method for 3D textured shapes.
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