SCORES

By Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi, Hao Zhang. 2018

124 citations recorded by the source corpus.

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss. We show results of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Implicit Fields for Generative Shape Modeling (2018, background): “There have been a variety of 3D shape representations for deep learning of shapes, such as voxel grids [9, 14, 28, 45, 46], octrees [18, 35, 40, 43, 44], multi-view images [29, 39], point clouds [1, 12, 13, 32, 33, 47, 48], geometry images [37, 38], deformable mesh/patches [16, 38, 42, 47], and part”
HyperCube: Implicit Field Representations of Voxelized 3D Models (2021, background): “3D objects can be represented using different approaches including voxel grids [4, 5, 6, 7], octrees [8, 9, 10, 11], multi-view images [12, 13, 14], point clouds [15, 16, 17, 18, 19], geometry images [20, 21], deformable meshes [5, 21, 22, 19], and part-based structural graphs [23, 24].”
ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis (2021, background): “The progresses achieved in the raster image domain has inspired researchers to synthesize 3D models [28], [29], 3D scenes [5], [30], 3D abstractions [31], and 3D motions for man-made objects [32].”
Learning to Infer and Execute 3D Shape Programs (2019, background): “Some other graphics papers also explore regularity, but without using programs (Mitra et al., 2013; Zhu et al., 2018; Nishida et al., 2018; Li et al., 2017).”
BSP-Net: Generating Compact Meshes via Binary Space Partitioning (2019, background): “There have been recent works on learning structured 3D models, in particular, linear [58] or hierarchical [23, 57, 29, 31] organization of part bounding boxes.”