FPCC: Fast Point Cloud Clustering-based Instance Segmentation for Industrial Bin-picking

  title={FPCC: Fast Point Cloud Clustering-based Instance Segmentation for Industrial Bin-picking},
  author={Yajun Xu and Shogo Arai and Diyi Liu and Fang-Erh Lin and Kazuhiro Kosuge},


A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
A method for training convolutional neural networks to predict instance segmentation results using synthetic data based on the SGPN framework is proposed and significantly outperforms the state-of-the-art method in both Stanford 3D Indoor Semantics Dataset and datasets.
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
3D-BoNet is a novel, conceptually simple and general framework for instance segmentation on 3D point clouds that surpasses existing work on both ScanNet and S3DIS datasets while being approximately 10x more computationally efficient.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
A multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings.
3D Instance Segmentation via Multi-Task Metric Learning
This work proposes a novel method for instance label segmentation of dense 3D voxel grids that achieves state-of-the-art performance on the ScanNet 3D instance segmentation benchmark.
Associatively Segmenting Instances and Semantics in Point Clouds
This paper first introduces a simple and flexible framework to segment instances and semantics in point clouds simultaneously, and proposes two approaches which make the two tasks take advantage of each other, leading to a win-win situation.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
Graph Attention Convolution for Point Cloud Semantic Segmentation
A novel graph attention convolution, whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object, which can capture the structured features of point clouds for fine-grained segmentation and avoid feature contamination between objects.
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point
Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
This is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning-based approaches and is one of the largest public datasets for object poses estimation in general.
Panoptic Feature Pyramid Networks
This work endsow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone, and shows it is a robust and accurate baseline for both tasks.