Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data

Rongrong Gao; Na Fan; Changlin Li; Wentao Liu; Qifeng Chen

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arXiv · 2108.03649

Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data

Rongrong Gao·Na Fan·Changlin Li·Wentao Liu·Qifeng Chen

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Abstract

We present a novel approach to joint depth and normal estimation for time-of-flight (ToF) sensors. Our model learns to predict the high-quality depth and normal maps jointly from ToF raw sensor data. To achieve this, we meticulously constructed the first large-scale dataset (named ToF-100) with paired raw ToF data and ground-truth high-resolution depth maps provided by an industrial depth camera. In addition, we also design a simple but effective framework for joint depth and normal estimation, applying a robust Chamfer loss via jittering to improve the performance of our model. Our experiments demonstrate that our proposed method can efficiently reconstruct high-resolution depth and normal maps and significantly outperforms state-of-the-art approaches. Our code and data will be available at \url{https://github.com/hkustVisionRr/JointlyDepthNormalEstimation}

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Rongrong Gao, Na Fan, Changlin Li, Wentao Liu, Qifeng Chen. 2021-08-08. Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data. https://arxiv.org/abs/2108.03649

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