Dense Voxel 3D Reconstruction Using a Monocular Event Camera
Haodong Chen Vera Chung Li Tan Xiaoming Chen
Event cameras are sensors inspired by biological systems that specialize in
capturing changes in brightness. These emerging cameras offer many advantages
over conventional frame-based cameras, including high dynamic range, high frame
rates, and extremely low power consumption. Due to these advantages, event
cameras have increasingly been adapted in various fields, such as frame
interpolation, semantic segmentation, odometry, and SLAM. However, their
application in 3D reconstruction for VR applications is underexplored. Previous
methods in this field mainly focused on 3D reconstruction through depth map
estimation. Methods that produce dense 3D reconstruction generally require
multiple cameras, while methods that utilize a single event camera can only
produce a semi-dense result. Other single-camera methods that can produce dense
3D reconstruction rely on creating a pipeline that either incorporates the
aforementioned methods or other existing Structure from Motion (SfM) or
Multi-view Stereo (MVS) methods. In this paper, we propose a novel approach for
solving dense 3D reconstruction using only a single event camera. To the best
of our knowledge, our work is the first attempt in this regard. Our preliminary
results demonstrate that the proposed method can produce visually
distinguishable dense 3D reconstructions directly without requiring pipelines
like those used by existing methods. Additionally, we have created a synthetic
dataset with $39,739$ object scans using an event camera simulator. This
dataset will help accelerate other relevant research in this field.