Papers › Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising
Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising
Guanting Dong, Yueyi Zhang, Zhiwei Xiong
Time-of-Flight (ToF) sensors have been increasingly used on mobile devices for depth sensing. However, the existence of noise, such as Multi-Path Interference (MPI) and shot noise, degrades the ToF imaging quality. Previous CNN-based methods remove ToF depth noise without considering the spatial hierarchical structure of the scene, which leads to failures in obtaining high quality depth images from a complex scene. In this paper, we propose a Spatial Hierarchy Aware Residual Pyramid Network, called SHARP-Net, to remove the depth noise by fully exploiting the geometry information of the scene on different scales. SHARP-Net first introduces a Residual Regression Module, which utilizes the depth images and amplitude images as the input, to calculate the depth residual progressively. Then, a Residual Fusion Module, summing over depth residuals from all scales, is imported to fuse multi-scale geometry information. Finally, shot noise is further eliminated by a Kernel Prediction Network. Experimental results demonstrate that our method significantly outperforms state-of-the-art ToF depth denoising methods on both synthetic and realistic datasets. The source code is available at https://github.com/ashesknight/tof-mpi-remove .
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections