# Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging

arXiv: https://arxiv.org/abs/1810.01930 · HTML: https://mlsanity.com/paper/1810.01930/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [DepthMaster: Taming Diffusion Models for Monocular Depth Estimation](https://mlsanity.com/paper/2501.02576/) (2025)
- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments](https://mlsanity.com/paper/2112.03750/) (2021)
- [EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes](https://mlsanity.com/paper/2310.08044/) (2023)

## Abstract (arXiv)

Depth sensing is useful in a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with minimal latency. However, for many battery-powered devices, the illumination source of a TOF camera is power hungry and can limit the battery life of the device. To address this issue, we present an algorithm that lowers the power for depth sensing by reducing the usage of the TOF camera and estimating depth maps using concurrently collected images. Our technique also adaptively controls the TOF camera and enables it when an accurate depth map cannot be estimated. To ensure that the overall system power for depth sensing is reduced, we design our algorithm to run on a low power embedded platform, where it outputs 640x480 depth maps at 30 frames per second. We evaluate our approach on several RGB-D datasets, where it produces depth maps with an overall mean relative error of 0.96% and reduces the usage of the TOF camera by 85%. When used with commercial TOF cameras, we estimate that our algorithm can lower the total power for depth sensing by up to 73%.