Monocular video depth estimation with planar constraint and semantic prior for real-time 3d reconstruction실시간 3차원 복원을 위해 평면 제약 조건 및 의미론적 사전 정보를 활용한 단안 영상에서의 깊이 정보 추정
We address the problem of 3D scene reconstruction from monocular video. Classical methods of scene
reconstruction suffer from high computational complexity, while learning-based methods have not yet
provided a general solution. In this work, we propose a novel algorithm for estimating consistent dense
depth maps from learning-based depth prior with planar constraint and a full framework 3D scene
reconstruction that consists of three main parts: 1) time efficient sparse visual SLAM optimization
algorithm, 2) dense depth estimation and 3) weighted depth fusion. Unlike previous works, our framework
provides real-time and robust performance that works in generalized, challenging and texture-poor scenes
without inference-time fine-tuning. The experiments on unseen on training indoor datasets show that
our framework outperforms state-of-the-art methods in terms of ”in the wild” accuracy and speed.