mlsanityData to 7 Oct 2026

Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion

arXiv 2202.12108 ↗First posted 24 Feb 2022cs.CVcited by 0

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Abstract (arXiv)

Depth estimation plays a important role in SLAM, odometry, and autonomous driving. Especially, monocular depth estimation is profitable technology because of its low cost, memory, and computation. However, it is not a sufficiently predicting depth map due to a camera often failing to get a clean image because of light conditions. To solve this problem, various sensor fusion method has been proposed. Even though it is a powerful method, sensor fusion requires expensive sensors, additional memory, and high computational performance. In this paper, we present color image and monochrome image pixel-level fusion and stereo matching with partially enhanced correlation coefficient maximization. Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation. We also validate the effectiveness of our design with an ablation study.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 24 Feb 2022.

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17 of 27 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  4. 2021Vision Transformers for Dense Predictioncited by 1,789
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  6. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  7. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  8. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  9. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 402
  10. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  11. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  12. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
5 more
  1. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 196
  2. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  3. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  4. 2020Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Reviewcited by 107
  5. 2016Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fieldscited by 17

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