Putree: A Photorealistic Large-Scale Virtual Benchmark for Forest Training

Yawen Lu, Yunhan Huang, Su Sun, Songlin Fei, Victor Chen

Abstract: Forest systems play an important role in mitigating anthropogenic climate change and regulating global climate. However, due to difficulties in collecting wild data and lack of forestry expertise, the availability of large-scale forest datasets is very limited. In this work, we establish a new virtual forest dataset named PUTree. Our goal is to create a larger, more photo-realistic and diverse dataset as a powerful training resource in the wild forest. Early experimental results demonstrate its validity as a new forest benchmark for the evaluation of tree detection and segmentation algorithms, and its potential to serve as virtual training environments in VR/AR applications.