Event cameras are a new type of sensors that are different from traditional cameras. Each pixel is triggered asynchronously by event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement of brightness is higher than a certain threshold, an event is output. Compared with traditional cameras, event cameras have the advantages of high dynamic range and no motion blur. Since events are caused by the apparent motion of intensity edges, the majority of 3D reconstructed maps consist only of scene edges, i.e., semi-dense maps, which is not enough for some applications. In this paper, we propose a pipeline to realize event-based dense reconstruction. First, deep learning is used to reconstruct intensity images from events. And then, structure from motion (SfM) is used to estimate camera intrinsic, extrinsic and sparse point cloud. Finally, multi-view stereo (MVS) is used to complete dense reconstruction.
@article{arxiv.2203.12270,
title = {Event-Based Dense Reconstruction Pipeline},
author = {Kun Xiao and Guohui Wang and Yi Chen and Jinghong Nan and Yongfeng Xie},
journal= {arXiv preprint arXiv:2203.12270},
year = {2022}
}