UC Santa Cruz

Precise ecological assessment of forest environments relies on acquiring dense, accurate spatial reconstructions. Point clouds serve as the leading form of three dimensional (3D) forest representation, and have been successfully used to recover important structural parameters of trees, namely diameter at breast height (DBH). Point cloud generation has traditionally relied on photogrammetric or Light Detection and Ranging (LiDAR) sensing modalities. Neural Radiance Fields (NeRFs), a recent innovation in artificial intelligence and computer vision, enable point cloud generation from a neural network trained on a sparse set of 2D images. This thesis presents an evaluation of three point cloud generation methods for the purpose of tree localization and DBH estimation in a coastal redwood forest. The first method uses lidar-inertial Simultaneous Localization and Mapping (SLAM) and serves as the state-of-the-art comparison. The two remaining methods generate neural-implicit scene geometry by training a NeRF on visual and SLAM data sourced from mobile phone and robot platforms. The results present an exciting avenue for rapid ecological assessment of forests environments using community-sourced mobile phone imagery as an alternative to expensive lidar sensing.