Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World

arXiv:2312.01117v2 Announce Type: replace
Abstract: To achieve strong real world performance, neural networks must be trained on large, diverse datasets; however, obtaining and annotating such datasets is costly and time-consuming, particularly for 3D point clouds. In this paper, we describe Paved2Paradise, a simple, cost-effective approach for generating fully labeled, diverse, and realistic lidar datasets from scratch, all while requiring minimal human annotation. Our key insight is that, by deliberately collecting separate "background" and "object" datasets (i.e., "factoring the real world"), …

abstract arxiv cost cs.cv cs.ro datasets diverse however lidar networks neural networks paper performance scalable simple simulation type world