Panoptic Segmentation
213 papers with code • 24 benchmarks • 32 datasets
Panoptic Segmentation is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions. In a given image, every pixel is assigned a semantic label, and pixels belonging to "things" classes (countable objects with instances, like cars and people) are assigned unique instance IDs. ( Image credit: Detectron2 )
Benchmarks
These leaderboards are used to track progress in Panoptic Segmentation
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Libraries
Use these libraries to find Panoptic Segmentation models and implementations
MS COCO
Cityscapes
KITTI
ScanNet
ADE20K
NYUv2
SemanticKITTI
SUN RGB-D
S3DIS
BDD100K
Latest papers with no code
Generalizable Entity Grounding via Assistance of Large Language Model
In this work, we propose a novel approach to densely ground visual entities from a long caption.
UrbanGenAI: Reconstructing Urban Landscapes using Panoptic Segmentation and Diffusion Models
In contemporary design practices, the integration of computer vision and generative artificial intelligence (genAI) represents a transformative shift towards more interactive and inclusive processes.
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians with Dual Feature Fusion
We propose Compact and Swift Segmenting 3D Gaussians(CoSSegGaussians), a method for compact 3D-consistent scene segmentation at fast rendering speed with only RGB images input.
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation
3D panoptic segmentation is a challenging perception task, especially in autonomous driving.
EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation
The use of autonomous robots for assistance tasks in hospitals has the potential to free up qualified staff and im-prove patient care.
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation
Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection.
Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
To address the challenge of the ``black-box" nature of deep learning in medical settings, we combine GCExplainer - an automated concept discovery solution - along with Logic Explained Networks to provide global explanations for Graph Neural Networks.
