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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on Real-Time Object Detection on COCO minival (MAP metric)
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
Ranked #1 on 3D Instance Segmentation on S3DIS (mIoU metric)
The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.
Ranked #5 on 3D Instance Segmentation on ScanNet(v2)
We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans.
Ranked #3 on 3D Semantic Instance Segmentation on ScanNetV2
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.
Ranked #8 on 3D Instance Segmentation on S3DIS
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.
Ranked #1 on 3D Object Detection on NYU Depth v2
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Ranked #2 on 3D Instance Segmentation on SceneNN
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Ranked #6 on 3D Object Detection on ScanNetV2