Para-Lane: Multi-Lane Dataset Registering Parallel Scans for Benchmarking Novel View Synthesis
Journal:
arXiv
Published Date:
Feb 21, 2025
Abstract
To evaluate end-to-end autonomous driving systems, a simulation environment
based on Novel View Synthesis (NVS) techniques is essential, which synthesizes
photo-realistic images and point clouds from previously recorded sequences
under new vehicle poses, particularly in cross-lane scenarios. Therefore, the
development of a multi-lane dataset and benchmark is necessary. While recent
synthetic scene-based NVS datasets have been prepared for cross-lane
benchmarking, they still lack the realism of captured images and point clouds.
To further assess the performance of existing methods based on NeRF and 3DGS,
we present the first multi-lane dataset registering parallel scans specifically
for novel driving view synthesis dataset derived from real-world scans,
comprising 25 groups of associated sequences, including 16,000 front-view
images, 64,000 surround-view images, and 16,000 LiDAR frames. All frames are
labeled to differentiate moving objects from static elements. Using this
dataset, we evaluate the performance of existing approaches in various testing
scenarios at different lanes and distances. Additionally, our method provides
the solution for solving and assessing the quality of multi-sensor poses for
multi-modal data alignment for curating such a dataset in real-world. We plan
to continually add new sequences to test the generalization of existing methods
across different scenarios. The dataset is released publicly at the project
page: https://nizqleo.github.io/paralane-dataset/.
