Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-
grained, instance-level, and hierarchical 3D part information. Our dataset consists of …
grained, instance-level, and hierarchical 3D part information. Our dataset consists of …
Meshcnn: a network with an edge
Polygonal meshes provide an efficient representation for 3D shapes. They explicitly
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …
Part‐based mesh segmentation: a survey
RSV Rodrigues, JFM Morgado… - Computer Graphics …, 2018 - Wiley Online Library
This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐
based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into …
based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into …
A scalable active framework for region annotation in 3d shape collections
Large repositories of 3D shapes provide valuable input for data-driven analysis and
modeling tools. They are especially powerful once annotated with semantic information such …
modeling tools. They are especially powerful once annotated with semantic information such …
Point2mesh: A self-prior for deformable meshes
In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …
A comprehensive overview of methodologies and performance evaluation frameworks in 3D mesh segmentation
P Theologou, I Pratikakis, T Theoharis - Computer Vision and Image …, 2015 - Elsevier
Abstract 3D mesh segmentation has become a crucial part of many applications in 3D shape
analysis. In this paper, a comprehensive survey on 3D mesh segmentation methods is …
analysis. In this paper, a comprehensive survey on 3D mesh segmentation methods is …
3D shape segmentation with projective convolutional networks
This paper introduces a deep architecture for segmenting 3D objects into their labeled
semantic parts. Our architecture combines image-based Fully Convolutional Networks …
semantic parts. Our architecture combines image-based Fully Convolutional Networks …
Learning shape abstractions by assembling volumetric primitives
We present a learning framework for abstracting complex shapes by learning to assemble
objects using 3D volumetric primitives. In addition to generating simple and geometrically …
objects using 3D volumetric primitives. In addition to generating simple and geometrically …
Primal-dual mesh convolutional neural networks
Recent works in geometric deep learning have introduced neural networks that allow
performing inference tasks on three-dimensional geometric data by defining convolution …
performing inference tasks on three-dimensional geometric data by defining convolution …
Multi-view intact space learning
It is practical to assume that an individual view is unlikely to be sufficient for effective multi-
view learning. Therefore, integration of multi-view information is both valuable and …
view learning. Therefore, integration of multi-view information is both valuable and …