The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1017–1023, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1017-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1017–1023, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1017-2022
 
30 May 2022
30 May 2022

MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS

H. Hristova, M. Abegg, C. Fischer, and N. Rehush H. Hristova et al.
  • Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland

Keywords: Monocular depth estimation, Forestry, Terrestrial laser scanning, Deep learning

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.