# Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1607.06349 · HTML: https://mlsanity.com/paper/1607.06349/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 10 of 10)

- [Boosting Light-Weight Depth Estimation Via Knowledge Distillation](https://mlsanity.com/paper/2105.06143/) (2021)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems](https://mlsanity.com/paper/2108.10506/) (2021)
- [Dense Depth Distillation with Out-of-Distribution Simulated Images](https://mlsanity.com/paper/2208.12464/) (2022)
- [DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation](https://mlsanity.com/paper/2303.02968/) (2023)
- [Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation](https://mlsanity.com/paper/2308.14400/) (2023)
- [Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data](https://mlsanity.com/paper/2409.16063/) (2024)
- [MSFNet:Multi-scale features network for monocular depth estimation](https://mlsanity.com/paper/2107.06445/) (2021)
- [360^circ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer](https://mlsanity.com/paper/2304.07967/) (2023)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~300Hz), without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo-realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.