# SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation

arXiv: https://arxiv.org/abs/1905.08598 · HTML: https://mlsanity.com/paper/1905.08598/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 30 of 59)

- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://mlsanity.com/paper/2203.15132/) (2022)
- [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://mlsanity.com/paper/2201.07436/) (2022)
- [DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image](https://mlsanity.com/paper/2311.18610/) (2023)
- [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://mlsanity.com/paper/2403.18807/) (2024)
- [Toward Practical Monocular Indoor Depth Estimation](https://mlsanity.com/paper/2112.02306/) (2021)
- [Single Image Depth Prediction with Wavelet Decomposition](https://mlsanity.com/paper/2106.02022/) (2021)
- [PlaneTR: Structure-Guided Transformers for 3D Plane Recovery](https://mlsanity.com/paper/2107.13108/) (2021)
- [IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty](https://mlsanity.com/paper/2210.03676/) (2022)
- [Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction](https://mlsanity.com/paper/2303.09792/) (2023)
- [OmniControlNet: Dual-stage Integration for Conditional Image Generation](https://mlsanity.com/paper/2406.05871/) (2024)
- [From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction](https://mlsanity.com/paper/2203.08122/) (2022)
- [Depth Map Decomposition for Monocular Depth Estimation](https://mlsanity.com/paper/2208.10762/) (2022)
- [Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey](https://mlsanity.com/paper/2406.19675/) (2024)
- [Boundary-induced and scene-aggregated network for monocular depth prediction](https://mlsanity.com/paper/2102.13258/) (2021)
- [PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image](https://mlsanity.com/paper/2110.11219/) (2021)
- [Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer](https://mlsanity.com/paper/2208.12527/) (2022)
- [Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.05315/) (2022)
- [Lightweight Monocular Depth with a Novel Neural Architecture Search Method](https://mlsanity.com/paper/2108.11105/) (2021)
- [NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis](https://mlsanity.com/paper/2112.12577/) (2021)
- [Egocentric Scene Understanding via Multimodal Spatial Rectifier](https://mlsanity.com/paper/2207.07077/) (2022)
- [FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene](https://mlsanity.com/paper/2307.14624/) (2023)
- [Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation](https://mlsanity.com/paper/2204.02386/) (2022)
- [Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis](https://mlsanity.com/paper/2401.17862/) (2024)
- [Compact and Optimal Deep Learning with Recurrent Parameter Generators](https://mlsanity.com/paper/2107.07110/) (2021)
- [Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis](https://mlsanity.com/paper/2109.14306/) (2021)
- [NC-SDF: Enhancing Indoor Scene Reconstruction Using Neural SDFs with View-Dependent Normal Compensation](https://mlsanity.com/paper/2405.00340/) (2024)
- [MT-ORL: Multi-Task Occlusion Relationship Learning](https://mlsanity.com/paper/2108.05722/) (2021)
- [Monocular Depth Estimation Using Cues Inspired by Biological Vision Systems](https://mlsanity.com/paper/2204.10384/) (2022)

## Abstract (arXiv)

We introduce SharpNet, a method that predicts an accurate depth map for an input color image, with a particular attention to the reconstruction of occluding contours: Occluding contours are an important cue for object recognition, and for realistic integration of virtual objects in Augmented Reality, but they are also notoriously difficult to reconstruct accurately. For example, they are a challenge for stereo-based reconstruction methods, as points around an occluding contour are visible in only one image. Inspired by recent methods that introduce normal estimation to improve depth prediction, we introduce a novel term that constrains depth and occluding contours predictions. Since ground truth depth is difficult to obtain with pixel-perfect accuracy along occluding contours, we use synthetic images for training, followed by fine-tuning on real data. We demonstrate our approach on the challenging NYUv2-Depth dataset, and show that our method outperforms the state-of-the-art along occluding contours, while performing on par with the best recent methods for the rest of the images. Its accuracy along the occluding contours is actually better than the `ground truth' acquired by a depth camera based on structured light. We show this by introducing a new benchmark based on NYUv2-Depth for evaluating occluding contours in monocular reconstruction, which is our second contribution.