# Very Power Efficient Neural Time-of-Flight

arXiv: https://arxiv.org/abs/1812.08125 · HTML: https://mlsanity.com/paper/1812.08125/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging](https://mlsanity.com/paper/2103.07087/) (2021)
- [RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising](https://mlsanity.com/paper/2111.15513/) (2021)
- [Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data](https://mlsanity.com/paper/2108.03649/) (2021)
- [Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor](https://mlsanity.com/paper/2409.08277/) (2024)
- [Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention](https://mlsanity.com/paper/2506.23542/) (2025)
- [Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond](https://mlsanity.com/paper/2511.01704/) (2025)
- [Near Field iToF LIDAR Depth Improvement from Limited Number of Shots](https://mlsanity.com/paper/2304.07047/) (2023)
- [Towards Robust Time-of-Flight Depth Denoising with Confidence-Aware Diffusion Model](https://mlsanity.com/paper/2503.19448/) (2025)

## Abstract (arXiv)

Time-of-Flight (ToF) cameras require active illumination to obtain depth information thus the power of illumination directly affects the performance of ToF cameras. Traditional ToF imaging algorithms is very sensitive to illumination and the depth accuracy degenerates rapidly with the power of it. Therefore, the design of a power efficient ToF camera always creates a painful dilemma for the illumination and the performance trade-off. In this paper, we show that despite the weak signals in many areas under extreme short exposure setting, these signals as a whole can be well utilized through a learning process which directly translates the weak and noisy ToF camera raw to depth map. This creates an opportunity to tackle the aforementioned dilemma and make a very power efficient ToF camera possible. To enable the learning, we collect a comprehensive dataset under a variety of scenes and photographic conditions by a specialized ToF camera. Experiments show that our method is able to robustly process ToF camera raw with the exposure time of one order of magnitude shorter than that used in conventional ToF cameras. In addition to evaluating our approach both quantitatively and qualitatively, we also discuss its implication to designing the next generation power efficient ToF cameras. We will make our dataset and code publicly available.