MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS

Abstract. Depth estimation from a single image is a challenging task, especially inside the highly structured forest environment. In this paper, we propose a supervised deep learning model for monocular depth estimation based on forest imagery. We train our model on a new data set of forest RGB-D images that we collected using a terrestrial laser scanner. Alongside the input RGB image, our model uses a sparse depth channel as input to recover the dense depth information. The prediction accuracy of our model is significantly higher than that of state-of-the-art methods when applied in the context of forest depth estimation. Our model brings the RMSE down to 2.1 m, compared to 4 m and above for reference methods.

• Publication year

  2022

• Venue

  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2022-05-30

• Fields of study

  Not labeled

• Identifiers
  DOI 10.5194/isprs-archives-xliii-b2-2022-1017-2022
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

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