Ph.D. Research Proficiency Exam: Muhammad Arbab Arshad

Muhammad Arbab Arshad
Thursday, April 4, 2024 - 8:00am
Event Type: 

Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions

We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods often struggle to capture the complex details of plants, which is crucial for phenotyping and breeding studies. We evaluate three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth data. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score with 30 minutes of training on the GPU, highlighting the efficiency and accuracy of NeRFs in challenging environments. Additionally, we propose an early stopping technique for GPU training, leading to a 61.1% reduction in training time while containing the average F1 score loss to just 7.4%. This optimization process significantly enhances the speed and efficiency of 3D reconstruction using NeRFs. These findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.

Committee: Soumik Sarkar (major professor), Aditya Balu, Baskar Ganapathysubramanian, Adarsh Krishnamurthy and Robyn Lutz.

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