3D Shape Representation

26 papers with code • 0 benchmarks • 3 datasets

Image: MeshNet

Most implemented papers

Occupancy Networks: Learning 3D Reconstruction in Function Space

LMescheder/Occupancy-Networks CVPR 2019

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

Facebookresearch/deepsdf CVPR 2019

In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.

Learning Implicit Fields for Generative Shape Modeling

czq142857/implicit-decoder CVPR 2019

We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.

BSP-Net: Generating Compact Meshes via Binary Space Partitioning

czq142857/BSP-NET-original CVPR 2020

The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.

On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes

u2ni/ICLR2021 17 Sep 2020

Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.

Learning Discriminative 3D Shape Representations by View Discerning Networks

chengz3906/View-Discerning-Network 11 Aug 2018

In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.

MeshNet: Mesh Neural Network for 3D Shape Representation

iMoonLab/MeshNet 28 Nov 2018

However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.

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.

Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes

rpreen/superformula 18 Apr 2012

We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel conditions after being physically instantiated by a 3D printer.

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

gbouritsas/Neural3DMM ICCV 2019

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.