# Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

arXiv: https://arxiv.org/abs/1610.01706 · HTML: https://mlsanity.com/paper/1610.01706/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2016.

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

- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks](https://mlsanity.com/paper/2208.10761/) (2022)
- [ISETAuto: Detecting vehicles with depth and radiance information](https://mlsanity.com/paper/2101.01843/) (2021)
- [False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation](https://mlsanity.com/paper/2207.03513/) (2022)
- [Does depth estimation help object detection?](https://mlsanity.com/paper/2204.06512/) (2022)
- [Depth-guided Free-space Segmentation for a Mobile Robot](https://mlsanity.com/paper/2311.01966/) (2023)
- [Deep Learning-Based 3D Instance and Semantic Segmentation: A Review](https://mlsanity.com/paper/2406.13308/) (2024)
- [Leveraging the Third Dimension in Contrastive Learning](https://mlsanity.com/paper/2301.11790/) (2023)
- [Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images](https://mlsanity.com/paper/2503.17982/) (2025)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision including object detection and semantic segmentation. Although depth sensors such as the Microsoft Kinect have facilitated easy acquisition of such depth information, the vast majority of images used in vision tasks do not contain depth information. In this paper, we show that augmenting RGB images with estimated depth can also improve the accuracy of both object detection and semantic segmentation. Specifically, we first exploit the recent success of depth estimation from monocular images and learn a deep depth estimation model. Then we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation. Additionally, we propose an RGB-D semantic segmentation method which applies a multi-task training scheme: semantic label prediction and depth value regression. We test our methods on several datasets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably.