# Deep attention-based classification network for robust depth prediction

arXiv: https://arxiv.org/abs/1807.03959 · HTML: https://mlsanity.com/paper/1807.03959/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 30 of 36)

- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [DepthFM: Fast Monocular Depth Estimation with Flow Matching](https://mlsanity.com/paper/2403.13788/) (2024)
- [Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2112.13047/) (2021)
- [Hierarchical Normalization for Robust Monocular Depth Estimation](https://mlsanity.com/paper/2210.09670/) (2022)
- [RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions](https://mlsanity.com/paper/2310.15171/) (2023)
- [Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction](https://mlsanity.com/paper/2103.04216/) (2021)
- [SAPA: Similarity-Aware Point Affiliation for Feature Upsampling](https://mlsanity.com/paper/2209.12866/) (2022)
- [Less is More: Consistent Video Depth Estimation with Masked Frames Modeling](https://mlsanity.com/paper/2208.00380/) (2022)
- [Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics](https://mlsanity.com/paper/2202.03131/) (2022)
- [Discrete-Constrained Regression for Local Counting Models](https://mlsanity.com/paper/2207.09865/) (2022)
- [Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation](https://mlsanity.com/paper/2103.02451/) (2021)
- [DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium](https://mlsanity.com/paper/2304.03560/) (2023)
- [Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey](https://mlsanity.com/paper/2406.19675/) (2024)
- [ChiTransformer:Towards Reliable Stereo from Cues](https://mlsanity.com/paper/2203.04554/) (2022)
- [The Second Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2304.07051/) (2023)
- [The Third Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2404.16831/) (2024)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor](https://mlsanity.com/paper/2209.13106/) (2022)
- [Inverse Rendering of Translucent Objects using Physical and Neural Renderers](https://mlsanity.com/paper/2305.08336/) (2023)
- [Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior](https://mlsanity.com/paper/2102.05212/) (2021)
- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [The Fourth Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2504.17787/) (2025)
- [FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene](https://mlsanity.com/paper/2307.14624/) (2023)
- [Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation](https://mlsanity.com/paper/2407.08149/) (2024)
- [Unsupervised Monocular Depth Perception: Focusing on Moving Objects](https://mlsanity.com/paper/2108.13062/) (2021)
- [U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Prediction](https://mlsanity.com/paper/2210.07140/) (2022)
- [On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression](https://mlsanity.com/paper/2202.12369/) (2022)
- [Long Range Object-Level Monocular Depth Estimation for UAVs](https://mlsanity.com/paper/2302.08943/) (2023)
- [Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation](https://mlsanity.com/paper/2303.05050/) (2023)
- [Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss](https://mlsanity.com/paper/2108.11098/) (2021)

## Abstract (arXiv)

In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)? b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.