SEED4D: A Synthetic Ego-Exo Dynamic 4D Data Generator Driving Dataset and Benchmark

Marius Kästingschäfer, Théo Gieruc, Sebastian Bernhard, Dylan Campbell, Eldar Insafutdinov, Eyvaz Najafli, Thomas Brox; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7741-7753

Models for egocentric 3D and 4D reconstruction including few-shot interpolation and extrapolation settings can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of complex dynamic and multi-view data. To facilitate the development of 3D and 4D reconstruction methods in the autonomous driving context we propose a Synthetic Ego-Exo Dynamic 4D (SEED4D) data generator and dataset. We present a customizable easy-to-use data generator for spatio-temporal multi-view data creation. Our open-source data generator allows the creation of synthetic data for camera setups commonly used in the NuScenes KITTI360 and Waymo datasets. Additionally SEED4D encompasses two large-scale multi-view synthetic urban scene datasets. Our static (3D) dataset encompasses 212k inward- and outward-facing vehicle images from 2k scenes while our dynamic (4D) dataset contains 16.8M images from 10k trajectories each sampled at 100 points in time with egocentric images exocentric images and LiDAR data. The datasets and the data generator can be found here: https://seed4d.github.io