Guided Depth Super-Resolution by Deep Anisotropic Diffusion

Metzger, Nando; Daudt, Rodrigo Caye; Schindler, Konrad

doi:10.48550/arxiv.2211.11592

Cited by 1 publication

(1 citation statement)

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“…Early implementations of such solutions for RGB-D are already present in CV. In (Metzger et al, 2022), the authors used an approach that combines guided anisotropic diffusion with a deep convolutional network to obtain super-resolution depth (D) images with sharp edges from initial low-resolution D and guiding RGB images. The results were presented on arbitrary scenes, hence, it is to be investigated if the approach is directly applicable to plant phenotyping using TLSs with high-quality integrated RGB cameras.…”

Section: Laser Beam Sizementioning

confidence: 99%

Challenges and Recommendations for 3d Plant Phenotyping in Agriculture Using Terrestrial Lasers Scanners

Medic,

Bömer,

Paulus

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Active sensing with LiDAR, and terrestrial laser scanners (TLS) in particular, are increasingly being used in plant phenotyping for assessing structural or 3D geometrical plant traits. Although these technologies provide the unprecedented possibility for remote, non-destructive, automatable, and efficient estimation of plant geometry, their deployment does not come without challenges. In this publication, we present a systematic overview of all challenges impacting TLS-based 3D plant phenotyping. We provide actionable recommendations for the end users of the technology, as well as the research questions and possible directions that can contribute the most to resolving these challenges. We specifically focus on TLSs, as we detected a lack in the existing literature dedicated to this sensing system providing a unique compromise between data quality and resolution vs. measurement efficiency and covered volume. The presented discussions are based on the literature review and our own experience in estimating the structural traits of sugar beet and wheat in plant phenotyping experiments.

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Section: Laser Beam Sizementioning

confidence: 99%