SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving

TL;DR

SynWoodScape is a synthetic surround-view fisheye camera dataset for autonomous driving, providing comprehensive annotations and enabling multi-camera algorithm development, addressing limitations of previous real-world datasets.

Contribution

It introduces a synthetic dataset with multi-camera annotations and ground truth for optical flow and depth, enhancing research in surround-view perception.

Findings

Generated 80k annotated images for 10+ tasks

Enabled development of multi-camera surround-view algorithms

Addressed ground truth limitations of real-world datasets

Abstract

Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras with a 190{\deg} field of view cover the 360{\deg} around the vehicle. Due to its high radial distortion, the standard algorithms do not extend easily. Previously, we released the first public fisheye surround-view dataset named WoodScape. In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it. Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth. Secondly, WoodScape did not have all four cameras annotated simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed to obtain a unified output in birds-eye space, which is enabled in the new dataset. We implemented surround-view fisheye geometric projections in CARLA Simulator matching WoodScape's configuration and created SynWoodScape. We release 80k images from the synthetic dataset with annotations for 10+ tasks. We also release the baseline code and supporting scripts.