  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}

Computer Vision and Pattern Recognition;Machine Learning;Robotics