# MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification

arXiv: https://arxiv.org/abs/1907.11111 · HTML: https://mlsanity.com/paper/1907.11111/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 13 of 13)

- [Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics](https://mlsanity.com/paper/2202.03131/) (2022)
- [Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation](https://mlsanity.com/paper/2103.02451/) (2021)
- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2111.09692/) (2021)
- [On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression](https://mlsanity.com/paper/2202.12369/) (2022)
- [SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning](https://mlsanity.com/paper/2204.00734/) (2022)
- [PanDepth: Joint Panoptic Segmentation and Depth Completion](https://mlsanity.com/paper/2212.14180/) (2022)
- [TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth Estimation](https://mlsanity.com/paper/2402.14340/) (2024)
- [Using depth information and colour space variations for improving outdoor robustness for instance segmentation of cabbage](https://mlsanity.com/paper/2103.16923/) (2021)
- [Predicting Depth from Semantic Segmentation using Game Engine Dataset](https://mlsanity.com/paper/2106.15257/) (2021)
- [Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning](https://mlsanity.com/paper/2208.10390/) (2022)
- [MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples](https://mlsanity.com/paper/2505.12069/) (2025)

## Abstract (arXiv)

We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, thus, plays a vital role in advanced driver assistance systems and autonomous vehicles. Best results for the SIDE task so far have been achieved using deep CNNs. However, optimization of regression problems, such as estimating depth, is still a challenging task. For the related tasks of image classification and semantic segmentation, numerous CNN-based methods with robust training behavior have been proposed. Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task. The auxiliary task can be disabled at test-time to predict continuous depth values using the main regression branch more efficiently. We applied MultiDepth to road scenes and present results on the KITTI depth prediction dataset. In experiments, we were able to show that end-to-end multi-task learning with both, regression and classification, is able to considerably improve training and yield more accurate results.