Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

By Braden Hurl, Krzysztof Czarnecki, Steven L. Waslander. 2019

121 citations recorded by the source corpus.

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020, background): “No existing synthetic dataset or simulator addresses all of these limitations, including those that target outdoor scene understanding [8, 20, 23, 34, 38, 41, 55, 56, 58, 60, 65, 76, 77].”
Adapting Object Detectors with Conditional Domain Normalization (2020, methodology): “Different from existing works [46, 17] are specifically designed for point cloud data, our proposed CDN is a general adaptation framework that adapts both 2D image and 3D point cloud object detector through the conditional domain normalization.”
Synthetic Data for Deep Learning (2019, methodology): “Figure 12: Sample images from the PreSIL dataset [269]: (a) RGB image, (b) point cloud, (c) black-and-white depth map, (d) color depth map, (e) segmentation map, (f) stencil buffer.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “[173] release a large scale, virtual, multi-modal dataset with LiDAR data and visual camera.”