# Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation

arXiv: https://arxiv.org/abs/1803.03778 · HTML: https://mlsanity.com/paper/1803.03778/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2018.

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

- [A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding](https://mlsanity.com/paper/2103.04056/) (2021)
- [PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2203.05983/) (2022)
- [UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks](https://mlsanity.com/paper/2108.04584/) (2021)
- [Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation](https://mlsanity.com/paper/2402.10580/) (2024)
- [Deep Joint Source-Channel Coding for Multi-Task Network](https://mlsanity.com/paper/2109.05779/) (2021)
- [Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction](https://mlsanity.com/paper/2501.02006/) (2025)
- [Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images](https://mlsanity.com/paper/2503.17982/) (2025)
- [OOS-DSD: Improving Out-of-stock Detection in Retail Images using Auxiliary Tasks](https://mlsanity.com/paper/2510.16508/) (2025)
- [Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion](https://mlsanity.com/paper/2606.02979/) (2026)
- [Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks](https://mlsanity.com/paper/2609.03977/) (2026)

## Abstract (arXiv)

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.