# Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference

arXiv: https://arxiv.org/abs/1708.02287 · HTML: https://mlsanity.com/paper/1708.02287/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 24 of 24)

- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [DepthFM: Fast Monocular Depth Estimation with Flow Matching](https://mlsanity.com/paper/2403.13788/) (2024)
- [Exploring Relational Context for Multi-Task Dense Prediction](https://mlsanity.com/paper/2104.13874/) (2021)
- [Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2112.13047/) (2021)
- [Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar](https://mlsanity.com/paper/2304.07213/) (2023)
- [Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2108.08829/) (2021)
- [Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance](https://mlsanity.com/paper/2102.06685/) (2021)
- [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)
- [Boundary-induced and scene-aggregated network for monocular depth prediction](https://mlsanity.com/paper/2102.13258/) (2021)
- [FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene](https://mlsanity.com/paper/2307.14624/) (2023)
- [Unsupervised Monocular Depth Perception: Focusing on Moving Objects](https://mlsanity.com/paper/2108.13062/) (2021)
- [Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictions](https://mlsanity.com/paper/2411.18823/) (2024)
- [CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2107.13800/) (2021)
- [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)
- [Neural Network Normal Estimation and Bathymetry Reconstruction from Sidescan Sonar](https://mlsanity.com/paper/2206.07819/) (2022)
- [Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation](https://mlsanity.com/paper/2307.09929/) (2023)
- [Depth Estimation using Weighted-loss and Transfer Learning](https://mlsanity.com/paper/2404.07686/) (2024)
- [Predicting Depth from Semantic Segmentation using Game Engine Dataset](https://mlsanity.com/paper/2106.15257/) (2021)
- [A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation](https://mlsanity.com/paper/2208.11993/) (2022)
- [Lightweight Monocular Depth Estimation](https://mlsanity.com/paper/2212.11363/) (2022)
- [Monocular Depth Estimation with Global-Aware Discretization and Local Context Modeling](https://mlsanity.com/paper/2508.03186/) (2025)

## Abstract (arXiv)

Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to tackle these challenges, which learns the direct mapping from a color image to the corresponding depth map. First, we represent monocular depth estimation as a multi-category dense labeling task by contrast to the regression based formulation. In this way, we could build upon the recent progress in dense labeling such as semantic segmentation. Second, we fuse different side-outputs from our front-end dilated convolutional neural network in a hierarchical way to exploit the multi-scale depth cues for depth estimation, which is critical to achieve scale-aware depth estimation. Third, we propose to utilize soft-weighted-sum inference instead of the hard-max inference, transforming the discretized depth score to continuous depth value. Thus, we reduce the influence of quantization error and improve the robustness of our method. Extensive experiments on the NYU Depth V2 and KITTI datasets show the superiority of our method compared with current state-of-the-art methods. Furthermore, experiments on the NYU V2 dataset reveal that our model is able to learn the probability distribution of depth.