Browse > Computer Vision > Instance Segmentation > 3D Instance Segmentation

3D Instance Segmentation

11 papers with code · Computer Vision

Leaderboards

Greatest papers with code

PointCNN: Convolution On $\mathcal{X}$-Transformed Points

NeurIPS 2018 yangyanli/PointCNN

The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.

3D INSTANCE SEGMENTATION 3D PART SEGMENTATION

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

NeurIPS 2019 QingyongHu/Benchmark_results_3D_point_cloud

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 INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

NeurIPS 2019 Yang7879/3D-BoNet

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 INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Associatively Segmenting Instances and Semantics in Point Clouds

CVPR 2019 WXinlong/ASIS

A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed.

3D INSTANCE SEGMENTATION 3D SEMANTIC SEGMENTATION

SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

CVPR 2018 laughtervv/SGPN

Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.

3D INSTANCE SEGMENTATION 3D OBJECT DETECTION 3D SEMANTIC INSTANCE SEGMENTATION SCENE SEGMENTATION

GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud

CVPR 2019 ericyi/GSPN

We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.

3D INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation

12 Feb 2019art-programmer/MASC

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.

3D INSTANCE SEGMENTATION SEMANTIC SEGMENTATION