3D Shape Generation
30 papers with code • 0 benchmarks • 1 datasets
Image: Mo et al
These leaderboards are used to track progress in 3D Shape Generation
Most implemented papers
Learning elementary structures for 3D shape generation and matching
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
Combinatorial 3D Shape Generation via Sequential Assembly
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis.
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
LION: Latent Point Diffusion Models for 3D Shape Generation
To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Multi-chart Generative Surface Modeling
The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation.
3DN: 3D Deformation Network
Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target.