3D Semantic Instance Segmentation
6 papers with code • 2 benchmarks • 1 datasets
Image: 3D-SIS
ScanNet
Latest papers with no code
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Indeed, we show that it is possible to train dense segmentation models using only bounding box labels.
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization
We propose a novel 3D point cloud segmentation framework named SASO, which jointly performs semantic and instance segmentation tasks.
3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
3D Instance Segmentation via Multi-Task Metric Learning
The second goal is to learn instance information by densely estimating directional information of the instance's center of mass for each voxel.
RevealNet: Seeing Behind Objects in RGB-D Scans
Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry.
3D-BEVIS: Bird's-Eye-View Instance Segmentation
A lot of progress was made in the field of object classification and semantic segmentation.
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation
As a result, our framework can output both the semantic prediction and the instance prediction.
