Stereo vision, structured light, and time of flight (ToF) are different range imaging techniques that acquire a scene and provide different features such as a depth map and an amplitude image. Compared to other imaging techniques, ToF can measure depth with high speed and good precision according to state of the art. However, it faces multipath interference (MPI) problems that give rise to an error in radiance information. Exploiting the sparsity of the received signal, we solved the multipath interference problem with the help of compressed sensing sparse recovery algorithms with some modification such as applying positivity constraint and proximity constraint. The modification in the algorithm has increased its robustness and proved to be successful in detecting the interference up to two paths successfully. We validated the approach by providing experimental results on synthetic data with ground truth that demonstrated its efficiency and accuracy to give MPI free output. Moreover, we applied a modified sparse recovery algorithm to real data and compared the result with the state-of-the-art methods. It shows better performance in separating the direct path from the multipath component with high accuracy.

中文翻译：

---

通过压缩传感算法恢复飞行时间传感器的深度

立体视觉，结构光和飞行时间（ToF）是不同范围的成像技术，可获取场景并提供不同的功能，例如深度图和振幅图像。与其他成像技术相比，ToF可以根据最新技术快速，高精度地测量深度。但是，它面临着多径干扰（MPI）问题，这会导致辐射信息出错。利用接收信号的稀疏性，我们借助压缩感测稀疏恢复算法并进行了一些修改（例如，应用正约束和邻近约束）来解决了多径干扰问题。该算法的修改提高了其鲁棒性，并被证明可以成功地成功检测多达两条路径的干扰。我们通过提供具有地面真实性的合成数据的实验结果验证了该方法，证明了其效率和准确性，可以免费提供MPI输出。此外，我们对实际数据应用了改进的稀疏恢复算法，并将结果与​​最新方法进行了比较。在将直接路径与多路径组件高精度分离时，它显示出更好的性能。