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Found 9 papers, 4 papers with code
Learning based 2D Irregular Shape Packing
2D irregular shape packing is a necessary step to arrange UV patches of a 3D model within a texture atlas for memory-efficient appearance rendering in computer graphics.
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose
We collect a total of 26. 7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses.
Laplacian2Mesh: Laplacian-Based Mesh Understanding
Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation.
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape.
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.
D$^2$IM-Net: Learning Detail Disentangled Implicit Fields from Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
