# Deformable spatial propagation network for depth completion

arXiv: https://arxiv.org/abs/2007.04251 · HTML: https://mlsanity.com/paper/2007.04251/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

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

- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://mlsanity.com/paper/2103.00783/) (2021)
- [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)
- [Dynamic Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2202.09769/) (2022)
- [LRRU: Long-short Range Recurrent Updating Networks for Depth Completion](https://mlsanity.com/paper/2310.08956/) (2023)
- [Tri-Perspective View Decomposition for Geometry-Aware Depth Completion](https://mlsanity.com/paper/2403.15008/) (2024)
- [Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries](https://mlsanity.com/paper/2104.02253/) (2021)
- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration](https://mlsanity.com/paper/2411.19278/) (2024)
- [OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations](https://mlsanity.com/paper/2406.11711/) (2024)
- [Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion](https://mlsanity.com/paper/2203.09855/) (2022)
- [SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion](https://mlsanity.com/paper/2204.13635/) (2022)
- [DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network](https://mlsanity.com/paper/2108.12655/) (2021)
- [SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events](https://mlsanity.com/paper/2309.12842/) (2023)
- [RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion](https://mlsanity.com/paper/2309.00655/) (2023)
- [Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion](https://mlsanity.com/paper/2309.02043/) (2023)
- [RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2405.10357/) (2024)
- [Learning an Efficient Multimodal Depth Completion Model](https://mlsanity.com/paper/2208.10771/) (2022)
- [Robust Depth Enhancement via Polarization Prompt Fusion Tuning](https://mlsanity.com/paper/2404.04318/) (2024)
- [Digging Into Normal Incorporated Stereo Matching](https://mlsanity.com/paper/2402.18171/) (2024)
- [Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement](https://mlsanity.com/paper/2404.19294/) (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)
- [Revisiting Deformable Convolution for Depth Completion](https://mlsanity.com/paper/2308.01905/) (2023)
- [DVMN: Dense Validity Mask Network for Depth Completion](https://mlsanity.com/paper/2107.06709/) (2021)
- [Gated Cross-Attention Network for Depth Completion](https://mlsanity.com/paper/2309.16301/) (2023)
- [A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving](https://mlsanity.com/paper/2401.15902/) (2024)
- [Progressive Depth Decoupling and Modulating for Flexible Depth Completion](https://mlsanity.com/paper/2405.09342/) (2024)
- [DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model](https://mlsanity.com/paper/2503.23993/) (2025)
- [Training Self-Supervised Depth Completion Using Sparse Measurements and a Single Image](https://mlsanity.com/paper/2507.14845/) (2025)

## Abstract (arXiv)

Depth completion has attracted extensive attention recently due to the development of autonomous driving, which aims to recover dense depth map from sparse depth measurements. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art methods in this task, which adopt a linear propagation model to refine coarse depth maps with local context. However, the propagation of each pixel occurs in a fixed receptive field. This may not be the optimal for refinement since different pixel needs different local context. To tackle this issue, in this paper, we propose a deformable spatial propagation network (DSPN) to adaptively generates different receptive field and affinity matrix for each pixel. It allows the network obtain information with much fewer but more relevant pixels for propagation. Experimental results on KITTI depth completion benchmark demonstrate that our proposed method achieves the state-of-the-art performance.