3D Instance Segmentation
57 papers with code • 8 benchmarks • 13 datasets
Image: OccuSeg
Benchmarks
These leaderboards are used to track progress in 3D Instance Segmentation
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Libraries
Use these libraries to find 3D Instance Segmentation models and implementations
ScanNet
S3DIS
KITTI-360
PartNet
SceneNN
STPLS3D
ScanNet200
CREMI
FOR-instance
Most implemented papers
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
ericyi/GSPN
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CVPR 2019
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans.
MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation
We propose a new approach for 3D instance segmentation based on sparse convolution and point affinity prediction, which indicates the likelihood of two points belonging to the same instance.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
Yang7879/3D-BoNet
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NeurIPS 2019
The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
96lives/ssen
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We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning.
Learning Gaussian Instance Segmentation in Point Clouds
LiuShihHung/GICN
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This paper presents a novel method for instance segmentation of 3D point clouds.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images
zudi-lin/pytorch_connectomics
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Medical Image Computing and Computer Assisted Intervention 2020
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Learning Regional Purity for Instance Segmentation on 3D Point Clouds
dsc1126/RPGN
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European Conference on Computer Vision (ECCV) 2020
In this paper, we define a novel concept of “regional purity” as the percentage of neighboring points belonging to the same instance within a fixed-radius 3D space.
