D2NT: A High-Performing Depth-to-Normal Translator

• URL: http://arxiv.org/abs/2304.12031v1
• Date: Mon, 24 Apr 2023 12:08:03 GMT
• Title: D2NT: A High-Performing Depth-to-Normal Translator
• Authors: Yi Feng, Bohuan Xue, Ming Liu, Qijun Chen, Rui Fan
• Abstract summary: This paper presents a superfast depth-to-normal translator (D2NT) that can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient filter (DAG) and a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.
• Score: 14.936434857460622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.

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