CCCV 2017: Computer Vision pp 442-452 | Cite as
Monocular Dense Reconstruction Based on Direct Sparse Odometry
Abstract
Monocular dense reconstruction plays more and more important role in AR application. In this paper, we present a new reconstruction system, which combines the Direct Sparse Odometry (DSO) and dense reconstruction into a uniform framework. The DSO can successfully track and build a semi-dense map even in low texture environment. The dense reconstruction is built on the fast superpixel segmentation and location consistency. However, a big gap between the semi-dense map and the dense reconstruction still needs to be bridged. To this end, we develop several elaborate methods including map points selection strategy, container for data sharing, and coordinate system transforming. We compare our system with a state-of-the-art monocular dense reconstruction system DPPTAM. The comparison experiments run on the public monocular visual odometry dataset. The experimental results show that our system has better performance and can run robustly, effectively in indoor and outdoor scenarios.
Keywords
Monocular SLAM Dense reconstruction Map points selection Shared container Coordinate system transformingReferences
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