Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

Abstract

We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.

References in corpus (5)

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• Variational Approaches for Auto-Encoding Generative Adversarial Networks
• A Point Set Generation Network for 3D Object Reconstruction from a Single Image
• SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

Cited by in corpus (14)

• PointCNN: Convolution On $\mathcal{X}$-Transformed Points
• Hypergraph Spectral Analysis and Processing in 3D Point Cloud
• Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
• Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
• Im2Avatar: Colorful 3D Reconstruction from a Single Image
• AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
• MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
• Efficient Semantic Scene Completion Network with Spatial Group Convolution
• Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
• Point Cloud Segmentation based on Hypergraph Spectral Clustering
• From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
• VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
• 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations
• What Do Single-view 3D Reconstruction Networks Learn?