# SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition

arXiv: https://arxiv.org/abs/2502.12994 · HTML: https://mlsanity.com/paper/2502.12994/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [Learning Non-Lambertian Object Intrinsics across ShapeNet Categories](https://mlsanity.com/paper/1612.08510/) (2016)
- [LightDepth: Single-View Depth Self-Supervision from Illumination Decline](https://mlsanity.com/paper/2308.10525/) (2023)
- [WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters](https://mlsanity.com/paper/2308.11776/) (2023)
- [A Surface-normal Based Neural Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2303.07264/) (2023)
- [On the Uncertain Single-View Depths in Colonoscopies](https://mlsanity.com/paper/2112.08906/) (2021)
- [Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy](https://mlsanity.com/paper/2310.01663/) (2023)
- [EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer](https://mlsanity.com/paper/2308.02716/) (2023)
- [Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images](https://mlsanity.com/paper/2309.07390/) (2023)

## Built on by (most cited first, 3 of 3)

- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision](https://mlsanity.com/paper/2602.17785/) (2026)
- [Gaussian Process Prior Variational Autoencoder for Endoscopic Videos](https://mlsanity.com/paper/2606.19908/) (2026)

## Abstract (arXiv)

Purpose: Visual 3D scene reconstruction can support colonoscopy navigation. It can help in recognising which portions of the colon have been visualised and characterising the size and shape of polyps. This is still a very challenging problem due to complex illumination variations, including abundant specular reflections. We investigate how to effectively decouple light and depth in this problem. Methods: We introduce a self-supervised model that simultaneously characterises the shape and lighting of the visualised colonoscopy scene. Our model estimates shading, albedo, depth, and specularities (SHADeS) from single images. Unlike previous approaches (IID), we use a non-Lambertian model that treats specular reflections as a separate light component. The implementation of our method is available at https://github.com/RemaDaher/SHADeS. Results: We demonstrate on real colonoscopy images (Hyper Kvasir) that previous models for light decomposition (IID) and depth estimation (MonoVIT, ModoDepth2) are negatively affected by specularities. In contrast, SHADeS can simultaneously produce light decomposition and depth maps that are robust to specular regions. We also perform a quantitative comparison on phantom data (C3VD) where we further demonstrate the robustness of our model. Conclusion: Modelling specular reflections improves depth estimation in colonoscopy. We propose an effective self-supervised approach that uses this insight to jointly estimate light decomposition and depth. Light decomposition has the potential to help with other problems, such as place recognition within the colon.