Incremental Joint Learning of Depth, Pose and Implicit Scene
  Representation on Monocular Camera in Large-scale Scenes
Tianchen Deng Nailin Wang Chongdi Wang Shenghai Yuan Jingchuan Wang Danwei Wang Weidong Chen
Dense scene reconstruction for photo-realistic view synthesis has various
applications, such as VR/AR, autonomous vehicles. However, most existing
methods have difficulties in large-scale scenes due to three core challenges:
\textit{(a) inaccurate depth input.} Accurate depth input is impossible to get
in real-world large-scale scenes. \textit{(b) inaccurate pose estimation.} Most
existing approaches rely on accurate pre-estimated camera poses. \textit{(c)
insufficient scene representation capability.} A single global radiance field
lacks the capacity to effectively scale to large-scale scenes. To this end, we
propose an incremental joint learning framework, which can achieve accurate
depth, pose estimation, and large-scale scene reconstruction. A vision
transformer-based network is adopted as the backbone to enhance performance in
scale information estimation. For pose estimation, a feature-metric bundle
adjustment (FBA) method is designed for accurate and robust camera tracking in
large-scale scenes. In terms of implicit scene representation, we propose an
incremental scene representation method to construct the entire large-scale
scene as multiple local radiance fields to enhance the scalability of 3D scene
representation. Extended experiments have been conducted to demonstrate the
effectiveness and accuracy of our method in depth estimation, pose estimation,
and large-scale scene reconstruction.