3D Shape Generation

30 papers with code • 0 benchmarks • 1 datasets

Image: Mo et al

Most implemented papers

Learning elementary structures for 3D shape generation and matching

TheoDEPRELLE/AtlasNetV2 NeurIPS 2019

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

POSTECH-CVLab/Combinatorial-3D-Shape-Generation 16 Apr 2020

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

angeladai/cnncomplete CVPR 2017

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

yongheng1991/3D-point-capsule-networks CVPR 2019

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

daerduoCarey/structurenet 1 Aug 2019

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

nv-tlabs/LION 12 Oct 2022

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

sinhayan/surfnet CVPR 2017

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

lorenmt/vsl 17 May 2017

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

helibenhamu/multichart3dgans 6 Jun 2018

The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation.

3DN: 3D Deformation Network

laughtervv/3DN CVPR 2019

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.