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Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator
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  • Rui Fan ,
  • Hengli Wang ,
  • Bohuan Xue ,
  • Huaiyang Huang ,
  • Yuan Wang, ,
  • Ming Liu ,
  • Ioannis Pitas ,
  • Yuan Wang
Hengli Wang
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Bohuan Xue
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Huaiyang Huang
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Yuan Wang,
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Ioannis Pitas
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Yuan Wang
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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.
Jul 2021Published in IEEE Robotics and Automation Letters volume 6 issue 3 on pages 5405-5412. 10.1109/LRA.2021.3067308