2026-10-15T21:59:13Z https://data.mendeley.com/oai

oai:data.mendeley.com/ptpm3bbv3j.1 2026-03-16T22:56:08Z

涂, 智恒 virDepth Mendeley Data virDepth is a large-scale simulation-based dataset developed for object-centric monocular depth estimation in autonomous driving scenarios. It is designed to support the training and evaluation of target center depth prediction for urban road users, with a particular focus on vehicles and pedestrians under long-range perception conditions. The dataset is generated through a reproducible simulation pipeline and provides synchronized RGB images, depth-related annotations, and object-level labels required for object-centric metric depth estimation. Unlike conventional dense-depth datasets that primarily emphasize pixel-wise scene reconstruction, virDepth is constructed to facilitate controlled analysis of object-level distance perception, especially for small and distant targets in complex urban environments. virDepth supports research on target center depth estimation, object-centric perception, and long-range monocular depth analysis for autonomous driving. It is intended for academic research and algorithm evaluation. Autonomous Driving Multimodal Deep Learning Dataset doi:10.17632/ptpm3bbv3j.1 oai:data.mendeley.com/ptpm3bbv3j.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/ptpm3bbv3j 2026-03-16T22:56:08Z