# Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

arXiv: https://arxiv.org/abs/2008.01034 · HTML: https://mlsanity.com/paper/2008.01034/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 28 of 28)

- [Unsupervised Depth Completion with Calibrated Backprojection Layers](https://mlsanity.com/paper/2108.10531/) (2021)
- [R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes](https://mlsanity.com/paper/2108.04814/) (2021)
- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks](https://mlsanity.com/paper/2303.14840/) (2023)
- [Test-Time Adaptation for Depth Completion](https://mlsanity.com/paper/2402.03312/) (2024)
- [Monitored Distillation for Positive Congruent Depth Completion](https://mlsanity.com/paper/2203.16034/) (2022)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [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)
- [AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation](https://mlsanity.com/paper/2310.09739/) (2023)
- [ProtoDepth: Unsupervised Continual Depth Completion with Prototypes](https://mlsanity.com/paper/2503.12745/) (2025)
- [CroMo: Cross-Modal Learning for Monocular Depth Estimation](https://mlsanity.com/paper/2203.12485/) (2022)
- [Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting](https://mlsanity.com/paper/2304.03763/) (2023)
- [RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2405.10357/) (2024)
- [MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report](https://mlsanity.com/paper/2209.07057/) (2022)
- [Know your sensORs -- A Modality Study For Surgical Action Classification](https://mlsanity.com/paper/2203.08674/) (2022)
- [A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps](https://mlsanity.com/paper/2205.12918/) (2022)
- [Zero-shot Depth Completion via Test-time Alignment with Affine-invariant Depth Prior](https://mlsanity.com/paper/2502.06338/) (2025)
- [UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion](https://mlsanity.com/paper/2410.18074/) (2024)
- [MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results](https://mlsanity.com/paper/2304.13916/) (2023)
- [ETA: Energy-based Test-time Adaptation for Depth Completion](https://mlsanity.com/paper/2508.05989/) (2025)
- [Segmentation-guided Domain Adaptation for Efficient Depth Completion](https://mlsanity.com/paper/2210.09213/) (2022)
- [Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor](https://mlsanity.com/paper/2409.08277/) (2024)
- [LiDAR Meta Depth Completion](https://mlsanity.com/paper/2307.12761/) (2023)
- [Generative Data Augmentation for Object Point Cloud Segmentation](https://mlsanity.com/paper/2505.17783/) (2025)
- [StarryGazer: Leveraging Monocular Depth Estimation Models for Domain-Agnostic Single Depth Image Completion](https://mlsanity.com/paper/2512.13147/) (2025)
- [On the Viability of Monocular Depth Pre-training for Semantic Segmentation](https://mlsanity.com/paper/2203.13987/) (2022)
- [MetaComp: Learning to Adapt for Online Depth Completion](https://mlsanity.com/paper/2207.10623/) (2022)
- [Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency](https://mlsanity.com/paper/2312.15263/) (2023)

## Abstract (arXiv)

Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB+LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.