# Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding

arXiv: https://arxiv.org/abs/2004.07944 · HTML: https://mlsanity.com/paper/2004.07944/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [PACO: Parts and Attributes of Common Objects](https://mlsanity.com/paper/2301.01795/) (2023)
- [Going Denser with Open-Vocabulary Part Segmentation](https://mlsanity.com/paper/2305.11173/) (2023)
- [MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation](https://mlsanity.com/paper/2503.13881/) (2025)
- [PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects](https://mlsanity.com/paper/2407.16696/) (2024)
- [Part-Based Models Improve Adversarial Robustness](https://mlsanity.com/paper/2209.09117/) (2022)
- [Visual Recognition by Request](https://mlsanity.com/paper/2207.14227/) (2022)
- [PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition](https://mlsanity.com/paper/2407.10918/) (2024)
- [Multi-task Fusion for Efficient Panoptic-Part Segmentation](https://mlsanity.com/paper/2212.07671/) (2022)
- [JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation](https://mlsanity.com/paper/2311.18618/) (2023)
- [OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments](https://mlsanity.com/paper/2606.29786/) (2026)
- [Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs](https://mlsanity.com/paper/2303.01991/) (2023)
- [PartImageNet++ Dataset: Enhancing Visual Models with High-Quality Part Annotations](https://mlsanity.com/paper/2601.01454/) (2026)
- [COCOTree: A Dataset and Benchmark for Open Tree-Structured Visual Decomposition](https://mlsanity.com/paper/2605.22068/) (2026)

## Abstract (arXiv)

In this technical report, we present two novel datasets for image scene understanding. Both datasets have annotations compatible with panoptic segmentation and additionally they have part-level labels for selected semantic classes. This report describes the format of the two datasets, the annotation protocols, the merging strategies, and presents the datasets statistics. The datasets labels together with code for processing and visualization will be published at https://github.com/tue-mps/panoptic_parts.