# 3D Shape Segmentation via Shape Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1702.08675 · HTML: https://mlsanity.com/paper/1702.08675/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2017.

## Built on by (most cited first, 15 of 15)

- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [BRepNet: A topological message passing system for solid models](https://mlsanity.com/paper/2104.00706/) (2021)
- [Contrastive Boundary Learning for Point Cloud Segmentation](https://mlsanity.com/paper/2203.05272/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data](https://mlsanity.com/paper/2110.10640/) (2021)
- [Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes](https://mlsanity.com/paper/2109.00452/) (2021)
- [LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation](https://mlsanity.com/paper/2103.14273/) (2021)
- [TexVocab: Texture Vocabulary-conditioned Human Avatars](https://mlsanity.com/paper/2404.00524/) (2024)
- [PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2101.11987/) (2021)
- [MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes](https://mlsanity.com/paper/2307.01115/) (2023)
- [Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey](https://mlsanity.com/paper/2402.12923/) (2024)
- [iSeg3D: An Interactive 3D Shape Segmentation Tool](https://mlsanity.com/paper/2112.12988/) (2021)
- [Scalable 3D Semantic Segmentation for Gun Detection in CT Scans](https://mlsanity.com/paper/2112.03917/) (2021)
- [Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields](https://mlsanity.com/paper/2203.11537/) (2022)
- [A Quantum-Powered Photorealistic Rendering](https://mlsanity.com/paper/2211.03418/) (2022)

## Abstract (arXiv)

We desgin a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation} with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes} which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.