SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark
Marius K\"astingsch\"afer, Th\'eo Gieruc, Sebastian Bernhard, Dylan Campbell, Eldar Insafutdinov, Eyvaz Najafli, Thomas Brox

TL;DR
SEED4D introduces a customizable synthetic data generator and large-scale datasets for egocentric and exocentric 3D/4D reconstruction in autonomous driving, enabling improved model training and evaluation.
Contribution
It provides the first open-source, flexible synthetic data generator and extensive datasets for dynamic multi-view urban scene reconstruction in autonomous driving.
Findings
Created 212k static vehicle images from 2k scenes.
Generated 16.8M dynamic images from 10k trajectories.
Facilitates development of 3D/4D reconstruction models.
Abstract
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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Computer Graphics and Visualization Techniques
