# Depth Extraction from Video Using Non-parametric Sampling

arXiv: https://arxiv.org/abs/2002.04479 · HTML: https://mlsanity.com/paper/2002.04479/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction](https://mlsanity.com/paper/2203.08122/) (2022)
- [Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images](https://mlsanity.com/paper/2402.05869/) (2024)
- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Absolute distance prediction based on deep learning object detection and monocular depth estimation models](https://mlsanity.com/paper/2111.01715/) (2021)
- [Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos](https://mlsanity.com/paper/2106.03505/) (2021)
- [NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis](https://mlsanity.com/paper/2112.12577/) (2021)
- [Underground Mapping and Localization Based on Ground-Penetrating Radar](https://mlsanity.com/paper/2409.16446/) (2024)
- [HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands](https://mlsanity.com/paper/2103.04845/) (2021)
- [Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking](https://mlsanity.com/paper/2112.14016/) (2021)
- [Joint Learning of Frequency and Spatial Domains for Dense Predictions](https://mlsanity.com/paper/2202.08991/) (2022)
- [RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classes](https://mlsanity.com/paper/2602.09532/) (2026)

## Abstract (arXiv)

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large dataset containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.