# FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene arXiv: https://arxiv.org/abs/2307.14624 ยท HTML: https://mlsanity.com/paper/2307.14624/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 46 references that are in the map. - First posted on arXiv 27 Jul 2023. ## Builds on (most cited first, 29 of 29) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016) - [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019) - [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014) - [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016) - [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018) - [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023) - [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020) - [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016) - [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019) - [DIODE: A Dense Indoor and Outdoor DEpth Dataset](https://mlsanity.com/paper/1908.00463/) (2019) - [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016) - [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022) - [NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation](https://mlsanity.com/paper/2203.01502/) (2022) - [Evaluation of CNN-based Single-Image Depth Estimation Methods](https://mlsanity.com/paper/1805.01328/) (2018) - [P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior](https://mlsanity.com/paper/2204.02091/) (2022) - [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://mlsanity.com/paper/2203.15132/) (2022) - [IM2CAD](https://mlsanity.com/paper/1608.05137/) (2016) - [PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image](https://mlsanity.com/paper/1804.06278/) (2018) - [SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation](https://mlsanity.com/paper/1905.08598/) (2019) - [Deep attention-based classification network for robust depth prediction](https://mlsanity.com/paper/1807.03959/) (2018) - [Single Image Depth Estimation: An Overview](https://mlsanity.com/paper/2104.06456/) (2021) - [Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks](https://mlsanity.com/paper/1809.00646/) (2018) - [Learning Depth from Single Images with Deep Neural Network Embedding Focal Length](https://mlsanity.com/paper/1803.10039/) (2018) - [Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference](https://mlsanity.com/paper/1708.02287/) (2017) - [Depth Map Decomposition for Monocular Depth Estimation](https://mlsanity.com/paper/2208.10762/) (2022) - [Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding](https://mlsanity.com/paper/1906.03279/) (2019) ## Built on by (most cited first, 7 of 7) - [Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model](https://mlsanity.com/paper/2312.13252/) (2023) - [ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation](https://mlsanity.com/paper/2407.08187/) (2024) - [Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception](https://mlsanity.com/paper/2409.05834/) (2024) - [Unified Panoramic Geometry Estimation via Multi-View Foundation Models](https://mlsanity.com/paper/2605.26368/) (2026) - [FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2405.10885/) (2024) - [EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes](https://mlsanity.com/paper/2509.01206/) (2025) - [Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach](https://mlsanity.com/paper/2604.07664/) (2026) ## Abstract (arXiv) It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes. We observe that it is essentially due to not only the scale-ambiguous problem but also the focal-ambiguous problem that decreases the generalization ability of monocular depth estimation. That is, images may be captured by cameras of different focal lengths in scenes of different scales. In this paper, we develop a focal-and-scale depth estimation model to well learn absolute depth maps from single images in unseen indoor scenes. First, a relative depth estimation network is adopted to learn relative depths from single images with diverse scales/semantics. Second, multi-scale features are generated by mapping a single focal length value to focal length features and concatenating them with intermediate features of different scales in relative depth estimation. Finally, relative depths and multi-scale features are jointly fed into an absolute depth estimation network. In addition, a new pipeline is developed to augment the diversity of focal lengths of public datasets, which are often captured with cameras of the same or similar focal lengths. Our model is trained on augmented NYUDv2 and tested on three unseen datasets. Our model considerably improves the generalization ability of depth estimation by 41%/13% (RMSE) with/without data augmentation compared with five recent SOTAs and well alleviates the deformation problem in 3D reconstruction. Notably, our model well maintains the accuracy of depth estimation on original NYUDv2.