# SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation

arXiv: https://arxiv.org/abs/2008.09346 · HTML: https://mlsanity.com/paper/2008.09346/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion](https://mlsanity.com/paper/2203.09855/) (2022)
- [DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network](https://mlsanity.com/paper/2108.12655/) (2021)
- [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)
- [PDC: Piecewise Depth Completion utilizing Superpixels](https://mlsanity.com/paper/2107.06711/) (2021)
- [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)
- [Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching](https://mlsanity.com/paper/2210.01436/) (2022)
- [DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model](https://mlsanity.com/paper/2503.23993/) (2025)
- [Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion](https://mlsanity.com/paper/2401.07439/) (2024)
- [SDformer: Efficient End-to-End Transformer for Depth Completion](https://mlsanity.com/paper/2409.08159/) (2024)

## Abstract (arXiv)

Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, data-driven learning approaches are widespread. Our work is inspired by latest trends in depth completion that tackle the problem of dense guidance for sparse information. We extend these ideas and create a generic cross-domain architecture that can be applied for a multitude of interpolation problems like optical flow, scene flow, or depth completion. In our experiments, we show that our proposed concept of Sparse Spatial Guided Propagation (SSGP) achieves improvements to robustness, accuracy, or speed compared to specialized algorithms.