Abstract
This paper proposes three-filters-to-normal (3F2N), an accurate and
ultrafast surface normal estimator (SNE), which is designed for
structured range sensor data, e.g., depth/disparity images. 3F2N SNE
computes surface normals by simply performing three filtering operations
(two image gradient filters in horizontal and vertical directions,
respectively, and a mean/median filter) on an inverse depth image or a
disparity image. Despite the simplicity of 3F2N SNE, no similar method
already exists in the literature. To evaluate the performance of our
proposed SNE, we created three large-scale synthetic datasets (easy,
medium and hard) using 24 3D mesh models, each of which is used to
generate 1800–2500 pairs of depth images (resolution: 480X640 pixels)
and the corresponding ground-truth surface normal maps from different
views. 3F2N SNE demonstrates the state-of-the-art performance,
outperforming all other existing geometry-based SNEs, where the average
angular errors with respect to the easy, medium and hard datasets are
1.66 degrees, 5.69 degrees and 15.31 degrees, respectively. Furthermore,
our C++ and CUDA implementations achieve a processing speed of over 260
Hz and 21 kHz, respectively. Our datasets and source code are publicly
available at sites.google.com/view/3f2n.