using a recursive neural network.

Input Another novel feature of our method, compared to previous works on hole filling and shape completion, is Furthermore, we used heuristic erosion of the input parts, but we believe it is possible to extend this approach to learn a natural erosion operation. We uniformly sample points on the input parts and normalize the resulting point clouds. The details of the two PointNet++’s used in the two-branch part encoder is provided in the supplementary material. Encoder-decoder architectures (AtlasNet, PCN) tend to significantly change the input geometry (Figure 7g,i). Note that after blending, there might be seams between the boundary of the joint and the boundary of the parts. The joint synthesis network, designed to focus on joint regions,

We show in the paper that each MLP maps a local neighborhood around each point on the 2D patch to a local surface on the 3D shape. Adobe Research; People; Research; Publications; News; Careers; Programs; Videos; About; Search ; Matt Fisher Senior Research Scientist San Francisco. After finding corresponding loops as shown in Figure 9 (b), there are redundant regions of the synthesized joint mesh which need to be removed before we can do Poisson mesh blending. We also imagine that scanned 3D objects or environments will enable users wearing a virtual/augmented reality headset to insert and view new furniture in their own home or to view changes of a factory for industrial/facility maintenance applications. Note that there is a chance that the algorithm cannot find a loop, i.e., returning “None”. We demonstrate the efficacy of our approach by using it to transfer segmentation across shapes. We also demonstrate the disadvantages of naive approaches, such as assembling input parts without any joint synthesis (e), or directly synthesizing the entire shape (f) from aligned parts. To quantitatively evaluate our method, we take eroded input parts from the same test shape, and use that test shape as the ground truth. Figure 9 (e) shows the result of blending part A to part B directly without the joint.

Since the loops are directed, after splitting the joint mesh using the loops, each component can be marked as “inside the part” or “outside the part”. After the above two steps, we use an existing Poisson mesh blending algorithm [49] to merge the synthesized joint mesh with the aligned part meshes. In addition to the qualitative ablation study in Figure 5, we have done a quantitative ablation study as shown in Table 1.

Neural generative models employ various representations for the output shape, including point clouds [1], voxel grids [46, 51], octrees [41], deformations of a template mesh [12], 3D embeddings of 2D atlases [13], or combinations of the above [31]. Copyright © 2020 Adobe. Computer Graphics Machine Learning. For example, the second chair in the teaser of the paper, and the second row in Figure 10. Our networks are trained on ShapeNet part dataset [48].

Generating high-resolution outputs requires higher sampling of the voxel grid, which increases memory usage.

We keep repeating the above two steps for a fixed number of iterations (25, in our implementation). arXiv Vanity renders academic papers from arXiv as responsive web pages so you don’t have to squint at a PDF. We compare the result of directly synthesizing joints from uneroded parts with our result in Figure 6.

In ICML, Cited by: §A.5, §2, §3.2. As shown in Figure 5, the inputs to our part alignment network are crudely segmented parts from different objects. [16] has a goal more similar to ours. A defining feature of our learning-based part connection framework is that it focuses on joint synthesis, rather than learning to reconstruct whole shapes. Van Kaick, H. Zhang, D. Cohen-Or, and B. Chen, J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. If a part does not exist in an object, we use a zero vector instead. How do we get the 3D shape’s faces? About Me. We first fix the parameters of the IM-decoder and optimize. The details of the approach is described in the supplementary material. PointNet++ B: After that, the implicit surface produced by the joint synthesis network is discretized into a voxel grid and converted into a mesh using marching cubes [27].

Re: Apple Compressor for JPEG2000 MXF -> ProRes HQ? Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, DeepSDF: learning continuous signed distance functions for shape representation, PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, Example-based authoring of procedural modeling programs with structural and continuous variability, N. Schor, O. Katzier, H. Zhang, and D. Cohen-Or, CompoNet: learning to generate the unseen by part synthesis and composition, A. Sharf, M. Blumenkrants, A. Shamir, and D. Cohen-Or, SnapPaste: an interactive technique for easy mesh composition, M. Sung, H. Su, V. G. Kim, S. Chaudhuri, and L. Guibas, ComplementMe: weakly-supervised component suggestions for 3D modeling, J. Talton, L. Yang, R. Kumar, M. Lim, N. Goodman, and R. Měch, Learning design patterns with Bayesian grammar induction, H. Wang, N. Schor, R. Hu, H. Huang, D. Cohen-Or, and H. Huang, Global-to-local generative model for 3d shapes, P. Wang, Y. Liu, Y. Guo, C. Sun, and X. Tong, Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes, ACM Transactions on Graphics (SIGGRAPH Asia), Y. Wang, S. Asafi, O. We stipulate that in the absence of any mathematical formulation of how to connect parts, the most general and effective way to handle the immensely rich part connection varieties is to learn how parts are connected from a large variety of 3D shapes. As the point cloud encoders and the IM-decoder in our joint synthesis network require different learning rates for training, we first pre-train the two-branch point cloud encoders for 100 epochs by pairing it with a fully connected decoder [1] for reconstructing 3D shape parts. The input to our pipeline is a set of labeled parts, where each part is represented by a triangle mesh. Let us denote the loop in the input part as S and the loop in the synthesized joint as T. The joint is denoted as X. It can be regarded as a means of normalization to handle the wide variety of possible part connections.

For example, we can view the 2D sampled patches as an atlas of the 3D shape, which allows us to apply textures on the shape. The input parts in Figure 7 are from different objects (Figure 7a), and naively aligning them (Figure 7b) results in a mesh with a large number of boundary artifacts. A novel joint synthesis method to connect input parts into a plausible 3D object, via an implicit surface. We also consider two voxel-based shape completion baselines that can be applied to our problem: 3DCNN and 3DResNet. We demonstrate the performance of our “learning to connect” framework on a variety of 3D shapes, both qualitatively and quantitatively, through comprehensive comparative studies. Instead, we use the network trained on the chair category from ShapeNet, and test it on COSEG to demonstrate how well it generalizes to novel datasets. We show in Figure  3(b) the architecture of our joint synthesis network. Visualization & Computer Graphics, Marching cubes: a high resolution 3D surface construction algorithm, L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, Occupancy networks: learning 3D reconstruction in function space, N. Mitra, M. Wand, H. Zhang, D. Cohen-Or, and M. Bokeloh, Eurographics State-of-the-art Report (STAR), K. Mo, P. Guerrero, L. Yi, H. Su, P. Wonka, N. Mitra, and L. Guibas, StructureNet: hierarchical graph networks for 3D shape generation, S. Muralikrishnan, V. G. Kim, M. Fisher, and S. Chaudhuri, Shape unicode: a unified shape representation, J. J. Our joint synthesis network is a core component of the framework. Here, erosion puts the training and testing scenarios on a common footing by forcing both to process similarly eroded parts, instead of trying to synthetically simulate real-world cutting and joining errors for training. We can see that our results have the lowest errors in both measures. PointNet++ A: Copyright © 2020 Adobe. The default batch size is 1.

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