A real-time, hardware agnostic framework for close-up branch reconstruction using RGB data

TL;DR

This paper presents a real-time, hardware-agnostic RGB-based framework for accurate close-up 3D reconstruction of tree branches, aiding pruning decisions with high precision and efficiency.

Contribution

It introduces a novel RGB-only method for real-time 3D tree branch reconstruction that is hardware agnostic and effective for thin branches, overcoming limitations of point cloud methods.

Findings

Primary branch models with 4-5 mm accuracy

Secondary branch orientation with 15° accuracy

Real-time processing at 10 cm/s without accuracy loss

Abstract

Creating accurate 3D models of tree topology is an important task for tree pruning. The 3D model is used to decide which branches to prune and then to execute the pruning cuts. Previous methods for creating 3D tree models have typically relied on point clouds, which are often computationally expensive to process and can suffer from data defects, especially with thin branches. In this paper, we propose a method for actively scanning along a primary tree branch, detecting secondary branches to be pruned, and reconstructing their 3D geometry using just an RGB camera mounted on a robot arm. We experimentally validate that our setup is able to produce primary branch models with 4-5 mm accuracy and secondary branch models with 15 degrees orientation accuracy with respect to the ground truth model. Our framework is real-time and can run up to 10 cm/s with no loss in model accuracy or ability to detect secondary branches.