About

Image: MeshNet

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

Datasets

Latest papers without code

GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion

12 Apr 2021

By mapping the observed partial surface to the canonical space and completing it in this space, the output representation describes the garment's full configuration using a complete 3D mesh with the per-vertex canonical coordinate label.

3D SHAPE REPRESENTATION POSE ESTIMATION

DualConv: Dual Mesh Convolutional Networks for Shape Correspondence

23 Mar 2021

While graph convolutional networks have previously been proposed over mesh vertex data, in this paper we explore how these networks can be extended to the dual face-based representation of triangular meshes, where nodes represent triangular faces in place of vertices.

3D SHAPE REPRESENTATION

DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces

4 Nov 2020

Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.

3D SHAPE REPRESENTATION

Learning Occupancy Function from Point Clouds for Surface Reconstruction

22 Oct 2020

Unlike the previous methods, which predict point occupancy with fully-connected multi-layer networks, we adapt the point cloud deep learning architecture, Point Convolution Neural Network (PCNN), to build our learning model.

3D SHAPE REPRESENTATION

Training Data Generating Networks: Linking 3D Shapes and Few-Shot Classification

16 Oct 2020

We propose a novel meta-learning framework to jointly train the data generating network and other components.

3D SHAPE RECONSTRUCTION 3D SHAPE REPRESENTATION CLASSIFICATION FEW-SHOT LEARNING

3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications

27 Jul 2020

In the field of computer vision, unsupervised learning for 2D object generation has advanced rapidly in the past few years.

3D SHAPE REPRESENTATION

Local Implicit Grid Representations for 3D Scenes

CVPR 2020

Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.

3D SHAPE REPRESENTATION

Local Implicit Grid Representations for 3D Scenes

19 Mar 2020

Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.

3D SHAPE REPRESENTATION

Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

CVPR 2020

To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.

3D SHAPE REPRESENTATION GENERATING 3D POINT CLOUDS

Local Deep Implicit Functions for 3D Shape

CVPR 2020

The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.

3D SHAPE REPRESENTATION