# Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

arXiv: https://arxiv.org/abs/1807.10376 · HTML: https://mlsanity.com/paper/1807.10376/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 21 of 21)

- [On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks](https://mlsanity.com/paper/2303.14840/) (2023)
- [Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression](https://mlsanity.com/paper/2205.04565/) (2022)
- [Consistent Direct Time-of-Flight Video Depth Super-Resolution](https://mlsanity.com/paper/2211.08658/) (2022)
- [iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging](https://mlsanity.com/paper/2103.07087/) (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)
- [CroMo: Cross-Modal Learning for Monocular Depth Estimation](https://mlsanity.com/paper/2203.12485/) (2022)
- [RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising](https://mlsanity.com/paper/2111.15513/) (2021)
- [FloatingFusion: Depth from ToF and Image-stabilized Stereo Cameras](https://mlsanity.com/paper/2210.02785/) (2022)
- [Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data](https://mlsanity.com/paper/2108.03649/) (2021)
- [Self-Annotated 3D Geometric Learning for Smeared Points Removal](https://mlsanity.com/paper/2311.09029/) (2023)
- [Depth from Coupled Optical Differentiation](https://mlsanity.com/paper/2409.10725/) (2024)
- [Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction](https://mlsanity.com/paper/2111.14396/) (2021)
- [Weakly-Supervised Optical Flow Estimation for Time-of-Flight](https://mlsanity.com/paper/2210.05298/) (2022)
- [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)
- [Centimeter-Wave Free-Space Time-of-Flight Imaging](https://mlsanity.com/paper/2105.11606/) (2021)
- [MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data](https://mlsanity.com/paper/2110.04066/) (2021)
- [MPI Planar Correction of Pulse Based ToF Cameras](https://mlsanity.com/paper/2312.12064/) (2023)
- [Towards Robust Time-of-Flight Depth Denoising with Confidence-Aware Diffusion Model](https://mlsanity.com/paper/2503.19448/) (2025)
- [Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing](https://mlsanity.com/paper/2505.22025/) (2025)
- [Reliable iToF Depth Sensing via Sensor-Intrinsic Uncertainty Modeling and State-Space Restoration](https://mlsanity.com/paper/2609.05507/) (2026)

## Abstract (arXiv)

Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.