# Bilateral Propagation Network for Depth Completion

arXiv: https://arxiv.org/abs/2403.11270 · HTML: https://mlsanity.com/paper/2403.11270/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 59 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://mlsanity.com/paper/2103.00783/) (2021)
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://mlsanity.com/paper/1807.00275/) (2018)
- [Learning Depth with Convolutional Spatial Propagation Network](https://mlsanity.com/paper/1810.02695/) (2018)
- [DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image](https://mlsanity.com/paper/1812.00488/) (2018)
- [Learning Guided Convolutional Network for Depth Completion](https://mlsanity.com/paper/1908.01238/) (2019)
- [Deep Depth Completion of a Single RGB-D Image](https://mlsanity.com/paper/1803.09326/) (2018)
- [CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion](https://mlsanity.com/paper/1911.05377/) (2019)
- [Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints](https://mlsanity.com/paper/1910.06727/) (2019)
- [In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://mlsanity.com/paper/1802.00036/) (2018)
- [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://mlsanity.com/paper/2304.13030/) (2023)
- [RigNet: Repetitive Image Guided Network for Depth Completion](https://mlsanity.com/paper/2107.13802/) (2021)
- [Learning Affinity via Spatial Propagation Networks](https://mlsanity.com/paper/1710.01020/) (2017)
- [Learning Joint 2D-3D Representations for Depth Completion](https://mlsanity.com/paper/2012.12402/) (2020)
- [Dynamic Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2202.09769/) (2022)
- [Adaptive Context-Aware Multi-Modal Network for Depth Completion](https://mlsanity.com/paper/2008.10833/) (2020)
- [Dense Depth Posterior (DDP) from Single Image and Sparse Range](https://mlsanity.com/paper/1901.10034/) (2019)
- [Joint Image Filtering with Deep Convolutional Networks](https://mlsanity.com/paper/1710.04200/) (2017)
- [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018)
- [LRRU: Long-short Range Recurrent Updating Networks for Depth Completion](https://mlsanity.com/paper/2310.08956/) (2023)
- [Depth Coefficients for Depth Completion](https://mlsanity.com/paper/1903.05421/) (2019)

## Built on by (most cited first, 30 of 60)

- [Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation](https://mlsanity.com/paper/2412.14015/) (2024)
- [Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors](https://mlsanity.com/paper/2503.17316/) (2025)
- [Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion](https://mlsanity.com/paper/2412.13389/) (2024)
- [Depth Anything with Any Prior](https://mlsanity.com/paper/2505.10565/) (2025)
- [OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration](https://mlsanity.com/paper/2411.19278/) (2024)
- [DepthLab: From Partial to Complete](https://mlsanity.com/paper/2412.18153/) (2024)
- [Distilling Monocular Foundation Model for Fine-grained Depth Completion](https://mlsanity.com/paper/2503.16970/) (2025)
- [Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion](https://mlsanity.com/paper/2412.19225/) (2024)
- [MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources](https://mlsanity.com/paper/2601.22054/) (2026)
- [SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth Maps](https://mlsanity.com/paper/2409.10202/) (2024)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [A Simple yet Effective Test-Time Adaptation for Zero-Shot Monocular Metric Depth Estimation](https://mlsanity.com/paper/2412.14103/) (2024)
- [UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion](https://mlsanity.com/paper/2410.18074/) (2024)
- [Amodal Depth Anything: Amodal Depth Estimation in the Wild](https://mlsanity.com/paper/2412.02336/) (2024)
- [Radar-Guided Polynomial Fitting for Metric Depth Estimation](https://mlsanity.com/paper/2503.17182/) (2025)
- [Event-Driven Dynamic Scene Depth Completion](https://mlsanity.com/paper/2505.13279/) (2025)
- [Any to Full: Prompting Depth Anything for Depth Completion in One Stage](https://mlsanity.com/paper/2603.05711/) (2026)
- [SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion](https://mlsanity.com/paper/2503.01257/) (2025)
- [PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and Consistency](https://mlsanity.com/paper/2507.07374/) (2025)
- [ETA: Energy-based Test-time Adaptation for Depth Completion](https://mlsanity.com/paper/2508.05989/) (2025)
- [DOC-Depth: A novel approach for dense depth ground truth generation](https://mlsanity.com/paper/2502.02144/) (2025)
- [DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image](https://mlsanity.com/paper/2504.01596/) (2025)
- [Depth Completion as Parameter-Efficient Test-Time Adaptation](https://mlsanity.com/paper/2602.14751/) (2026)
- [Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor](https://mlsanity.com/paper/2409.08277/) (2024)
- [Learning Inverse Laplacian Pyramid for Progressive Depth Completion](https://mlsanity.com/paper/2502.07289/) (2025)
- [Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion](https://mlsanity.com/paper/2508.04984/) (2025)
- [AURORA-KITTI: Any-Weather Depth Completion and Denoising](https://mlsanity.com/paper/2603.14701/) (2026)
- [The Midas Touch for Metric Depth](https://mlsanity.com/paper/2605.11578/) (2026)
- [ARSGaussian: 3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis](https://mlsanity.com/paper/2412.18380/) (2024)
- [DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model](https://mlsanity.com/paper/2503.23993/) (2025)

## Abstract (arXiv)

Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagation-based, which work as an iterative refinement on the initial estimated dense depth. However, the initial depth estimations mostly result from direct applications of convolutional layers on the sparse depth map. In this paper, we present a Bilateral Propagation Network (BP-Net), that propagates depth at the earliest stage to avoid directly convolving on sparse data. Specifically, our approach propagates the target depth from nearby depth measurements via a non-linear model, whose coefficients are generated through a multi-layer perceptron conditioned on both radiometric difference and spatial distance. By integrating bilateral propagation with multi-modal fusion and depth refinement in a multi-scale framework, our BP-Net demonstrates outstanding performance on both indoor and outdoor scenes. It achieves SOTA on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. Experimental results not only show the effectiveness of bilateral propagation but also emphasize the significance of early-stage propagation in contrast to the refinement stage. Our code and trained models will be available on the project page.