The Cityscapes Dataset for Semantic Urban Scene Understanding (1604.01685v2)

Abstract: Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

• The paper introduces the Cityscapes dataset with 5,000 pixel-level and 20,000 coarse annotated images to advance urban scene understanding.
• It details a rigorous annotation process using automotive-grade stereo cameras, providing instance-level labels for objects like vehicles and humans.
• Benchmark evaluations using metrics such as iIoU and AP reveal significant challenges, guiding future research in autonomous driving and semantic segmentation.

Overview of the Cityscapes Dataset for Semantic Urban Scene Understanding

The paper, "The Cityscapes Dataset for Semantic Urban Scene Understanding," presents a comprehensive dataset aimed at advancing research in visual scene understanding, particularly within complex urban environments. The Cityscapes dataset provides extensive annotations that enable both pixel-level and instance-level labeling, addressing the need for accurate visual perception systems crucial for applications such as autonomous driving.

Dataset Composition and Annotation

The dataset consists of 5,000 high-quality pixel-level annotated images and 20,000 coarsely annotated images, captured from 50 different cities over various seasons. The annotations are meticulously detailed, covering 30 classes divided into eight categories including flat areas, construction, nature, vehicles, sky, objects, humans, and voids. Crucially, this dataset goes beyond mere pixel-level annotations by incorporating instance-level annotations for classes like humans and vehicles. This allows for robust training and benchmarking of algorithms designed for semantic segmentation and object detection in urban scenarios.

Data Acquisition and Quality

Data was captured using an automotive-grade stereo camera setup, ensuring high dynamic range and temporal consistency. The annotated data reflects realistic urban variability, including diverse lighting conditions and different levels of scene complexity. On average, annotating each image required significant manual effort, around 1.5 hours per image, highlighting the meticulous quality control undertaken by the authors.

Comparative Analysis

In terms of statistical volume, the Cityscapes dataset surpasses existing datasets such as KITTI, CamVid, and DUS, both in size and annotation richness. For instance, the dataset includes up to two orders of magnitude more annotated pixels than its counterparts, therefore providing a vast library of training data crucial for developing high-performance scene understanding models.

Benchmark Suite and Metrics

The paper introduces two main tasks for the benchmark suite: pixel-level semantic labeling and instance-level semantic labeling, each with specific evaluation metrics. For pixel-level labeling, the authors use standard IoU and a novel metric called instance-level IoU (iIoU), which normalizes the contribution of each object instance based on its size. The evaluation metrics for instance-level labeling include average precision (AP) measured across various IoU thresholds and object distances, thereby providing a fine-grained perspective on algorithm performance.

Baseline Evaluations

A key contribution of the paper is the detailed performance analysis of several state-of-the-art methods using the provided benchmarks. Baselines span various approaches from fully convolutional networks (FCNs) to methods integrating conditional random fields (CRFs) and recurrent neural networks (RNNs). The findings reveal that the diverse and complex nature of the Cityscapes dataset presents significant challenges, with observed performance rankings differing notably from results on more generic datasets like PASCAL VOC.

Implications and Future Directions

The insights derived from Cityscapes emphasize the necessity for datasets that capture the high variability and complexity of real-world urban scenes. This dataset sets a new standard, challenging existing models and directing future research towards more robust and generalizable solutions. Additionally, the paper hints at the potential for further research exploiting weakly labeled data, indicating a promising direction for leveraging the coarse annotations included in Cityscapes.

Conclusion

The Cityscapes dataset is a pivotal resource for advancing semantic urban scene understanding. Its scale, diversity, and detailed annotations offer a rigorous platform for training and evaluating advanced visual perception systems. By addressing the existing gaps in urban scene datasets, Cityscapes facilitates significant progress towards achieving reliable autonomous driving technologies and other real-world applications requiring precise scene understanding. Future developments may include enhancing the dataset with further fine-grained classes and exploring cross-dataset evaluation strategies to ensure algorithm robustness across varied environments.