Creating a Segmented Pointcloud of Grapevines by Combining Multiple Viewpoints Through Visual Odometry

TL;DR

This paper presents a computer vision pipeline that combines multiple viewpoints using visual odometry and segmentation to create a detailed 3D pointcloud of grapevines, aiding in automated pruning decisions.

Contribution

It introduces a novel pipeline integrating detectron2 segmentation and keypoint visual odometry for accurate 3D reconstruction of grapevines from multiple views.

Findings

Effective merging of multiple viewpoints into a unified pointcloud.

Enhanced accuracy in identifying pruning points.

Potential to reduce labor and improve consistency in grapevine pruning.

Abstract

Grapevine winter pruning is a labor-intensive and repetitive process that significantly influences the quality and quantity of the grape harvest and produced wine of the following season. It requires a careful and expert detection of the point to be cut. Because of its complexity, repetitive nature and time constraint, the task requires skilled labor that needs to be trained. This extended abstract presents the computer vision pipeline employed in project Vinum, using detectron2 as a segmentation network and keypoint visual odometry to merge different observation into a single pointcloud used to make informed pruning decisions.