SEED4D：合成式自-外动态4D数据生成器、驾驶数据集与基准
SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark
AI总结：
针对自动驾驶领域3D/4D重建缺乏多视角动态混合数据集的问题，提出SEED4D开源生成器及两个大规模城市场景数据集，支持主流驾驶数据集相机配置，提供多视角图像与激光雷达数据。
摘要：
以自我视角为输入的3D和4D重建模型（包括少样本插值与外插设置），可将来自外部视角的图像作为监督信号从中获益。现有数据集均无法提供复杂、动态且多视角数据的必要组合。为推动自动驾驶场景下3D与4D重建方法的发展，我们提出合成式自-外动态4D（SEED4D）数据生成器及数据集。我们推出一款可定制、易用的时空多视角数据创建生成器，这款开源生成器可针对NuScenes、KITTI360、Waymo数据集常用的相机配置生成合成数据。此外，SEED4D包含两个大规模多视角合成城市场景数据集：静态（3D）数据集涵盖2000个场景的21.2万张车辆向内、向外拍摄图像；动态（4D）数据集包含1万条轨迹的1680万张图像，每条轨迹在100个时间点采样，包含自我视角图像、外部视角图像及激光雷达数据。数据集与生成器可访问https://seed4d.github.io/获取。
英文摘要：
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 at https://seed4d.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
