mlsanityData to 14 Oct 2026

Real-time Monocular Depth Estimation on Embedded Systems

arXiv 2308.10569 ↗First posted 21 Aug 2023cs.CVcited by 6

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Abstract (arXiv)

Depth sensing is of paramount importance for unmanned aerial and autonomous vehicles. Nonetheless, contemporary monocular depth estimation methods employing complex deep neural networks within Convolutional Neural Networks are inadequately expedient for real-time inference on embedded platforms. This paper endeavors to surmount this challenge by proposing two efficient and lightweight architectures, RT-MonoDepth and RT-MonoDepth-S, thereby mitigating computational complexity and latency. Our methodologies not only attain accuracy comparable to prior depth estimation methods but also yield faster inference speeds. Specifically, RT-MonoDepth and RT-MonoDepth-S achieve frame rates of 18.4&30.5 FPS on NVIDIA Jetson Nano and 253.0&364.1 FPS on Jetson AGX Orin, utilizing a single RGB image of resolution 640x192. The experimental results underscore the superior accuracy and faster inference speed of our methods in comparison to existing fast monocular depth estimation methodologies on the KITTI dataset.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

12 of 18 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2018Group Normalizationcited by 1,524
  4. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  5. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  6. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  7. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  8. 2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
  9. 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
  10. 2019FastDepth: Fast Monocular Depth Estimation on Embedded Systemscited by 94
  11. 2021Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenescited by 89
  12. 2022Lightweight Monocular Depth Estimation through Guided Decodingcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimationcited by 10
  2. 2025QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edgecited by 8
  3. 2025Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environmentscited by 0
  4. 2025Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUscited by 0
  5. 2026Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimationcited by 0
  6. 2026EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusioncited by 0