# Lightweight Monocular Depth Estimation through Guided Decoding

arXiv: https://arxiv.org/abs/2203.04206 · HTML: https://mlsanity.com/paper/2203.04206/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [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)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [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)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Enforcing geometric constraints of virtual normal for depth prediction](https://mlsanity.com/paper/1907.12209/) (2019)
- [NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications](https://mlsanity.com/paper/1804.03230/) (2018)
- [High Quality Monocular Depth Estimation via Transfer Learning](https://mlsanity.com/paper/1812.11941/) (2018)
- [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://mlsanity.com/paper/1903.03273/) (2019)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [Towards real-time unsupervised monocular depth estimation on CPU](https://mlsanity.com/paper/1806.11430/) (2018)
- [Traffic Control Gesture Recognition for Autonomous Vehicles](https://mlsanity.com/paper/2007.16072/) (2020)
- [Real-time single image depth perception in the wild with handheld devices](https://mlsanity.com/paper/2006.05724/) (2020)
- [Adversarial Network Compression](https://mlsanity.com/paper/1803.10750/) (2018)
- [DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks](https://mlsanity.com/paper/1904.09007/) (2019)
- [Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware](https://mlsanity.com/paper/2108.02671/) (2021)

## Built on by (most cited first, 14 of 14)

- [Real-time Monocular Depth Estimation on Embedded Systems](https://mlsanity.com/paper/2308.10569/) (2023)
- [Deep Learning on Home Drone: Searching for the Optimal Architecture](https://mlsanity.com/paper/2209.11064/) (2022)
- [PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks](https://mlsanity.com/paper/2301.12914/) (2023)
- [LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning](https://mlsanity.com/paper/2211.08608/) (2022)
- [Lightweight Monocular Depth Estimation via Token-Sharing Transformer](https://mlsanity.com/paper/2306.05682/) (2023)
- [Visual Autoregressive Modelling for Monocular Depth Estimation](https://mlsanity.com/paper/2512.22653/) (2025)
- [Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation](https://mlsanity.com/paper/2405.17704/) (2024)
- [ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device](https://mlsanity.com/paper/2607.08771/) (2026)
- [DepthART: Scaling Foundation Monocular Depth to Tiny Models](https://mlsanity.com/paper/2607.17099/) (2026)
- [Lightweight Monocular Depth Estimation with an Edge Guided Network](https://mlsanity.com/paper/2209.14829/) (2022)
- [Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation](https://mlsanity.com/paper/2410.11610/) (2024)
- [LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment](https://mlsanity.com/paper/2505.00980/) (2025)
- [BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems](https://mlsanity.com/paper/2511.04388/) (2025)
- [Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environments](https://mlsanity.com/paper/2511.19396/) (2025)

## Abstract (arXiv)

We present a lightweight encoder-decoder architecture for monocular depth estimation, specifically designed for embedded platforms. Our main contribution is the Guided Upsampling Block (GUB) for building the decoder of our model. Motivated by the concept of guided image filtering, GUB relies on the image to guide the decoder on upsampling the feature representation and the depth map reconstruction, achieving high resolution results with fine-grained details. Based on multiple GUBs, our model outperforms the related methods on the NYU Depth V2 dataset in terms of accuracy while delivering up to 35.1 fps on the NVIDIA Jetson Nano and up to 144.5 fps on the NVIDIA Xavier NX. Similarly, on the KITTI dataset, inference is possible with up to 23.7 fps on the Jetson Nano and 102.9 fps on the Xavier NX. Our code and models are made publicly available.