Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Gianmarco Roggiolani Federico Magistri Tiziano Guadagnino Jens Behley Cyrill Stachniss
Crops for food, feed, fiber, and fuel are key natural resources for our
society. Monitoring plants and measuring their traits is an important task in
agriculture often referred to as plant phenotyping. Traditionally, this task is
done manually, which is time- and labor-intensive. Robots can automate
phenotyping providing reproducible and high-frequency measurements. Today's
perception systems use deep learning to interpret these measurements, but
require a substantial amount of annotated data to work well. Obtaining such
labels is challenging as it often requires background knowledge on the side of
the labelers. This paper addresses the problem of reducing the labeling effort
required to perform leaf instance segmentation on 3D point clouds, which is a
first step toward phenotyping in 3D. Separating all leaves allows us to count
them and compute relevant traits as their areas, lengths, and widths. We
propose a novel self-supervised task-specific pre-training approach to
initialize the backbone of a network for leaf instance segmentation. We also
introduce a novel automatic postprocessing that considers the difficulty of
correctly segmenting the points close to the stem, where all the leaves petiole
overlap. The experiments presented in this paper suggest that our approach
boosts the performance over all the investigated scenarios. We also evaluate
the embeddings to assess the quality of the fully unsupervised approach and see
a higher performance of our domain-specific postprocessing.