Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps
This work addresses surface normal estimation for applications like robotics and computer vision, but it appears incremental as it builds on existing methods with efficiency and accuracy improvements.
The paper tackles the problem of estimating surface normals from depth maps by proposing a fast, accurate, and boundary-aware method, achieving improved accuracy and real-time performance compared to baseline algorithms.
This paper proposes a fast and accurate surface normal estimation method which can be directly used on depth maps (organized point clouds). The surface normal estimation process is formulated as a closed-form expression. In order to reduce the effect of measurement noise, the averaging operation is utilized in multi-direction manner. The multi-direction normal estimation process is reformulated in the next step to be implemented efficiently. Finally, a simple yet effective method is proposed to remove erroneous normal estimation at depth discontinuities. The proposed method is compared to well-known surface normal estimation algorithms. The results show that the proposed algorithm not only outperforms the baseline algorithms in term of accuracy, but also is fast enough to be used in real-time applications.