Panoptic Segmentation
Panoptic segmentation is a task in the field of computer vision that aims to combine semantic segmentation and instance segmentation to provide a comprehensive understanding of a scene. Its goal is to segment an image into parts or regions with semantic meaning and to detect and distinguish individual object instances within these regions. Each pixel is assigned a semantic label, and pixels belonging to "thing" classes (such as countable object instances) are given a unique instance ID.
COCO test-dev
Mask DINO (single scale)
Cityscapes val
Panoptic FCN* (Swin-L, Cityscapes-fine)
COCO minival
OpenSeeD (SwinL, single-scale)
ADE20K val
DiNAT-L (Mask2Former, 640x640)
Mapillary val
OneFormer (DiNAT-L, single-scale)
Cityscapes test
EfficientPS
LaRS
Mask2Former (Swin-B)
S3DIS Area5
ScanNetV2
OneFormer3D
Indian Driving Dataset
EfficientPS
KITTI Panoptic Segmentation
EfficientPS
PanNuke
LKCell
ScanNet
OneFormer3D
PASTIS
Exchanger+Mask2Former
COCO panoptic
VAN-B6*
MUSES: MUlti-SEnsor Semantic perception dataset
NYU Depth v2
SemanticKITTI
P3Former
ADE20K
MasQCLIP
DALES
SuperCluster
Hypersim
KITTI-360
Panoptic nuScenes val
Panoptic nuScenes test
(AF)2-S3Net + CenterPoint
PASTIS-R
Early Fusion
S3DIS
SUN-RGBD