A 3D reconstruction platform for complex plants using OB-NeRF

Applying 3D reconstruction techniques to individual plants has enhanced high-throughput phenotyping and provided accurate data support for developing "digital twins" in the agricultural domain. High costs, slow processing times, intricate workflows, and limited automation often constrain the application of existing 3D reconstruction platforms.

We develop a 3D reconstruction platform for complex plants to overcome these issues. Initially, a video acquisition system is built based on "camera to plant" mode. Then, we extract the keyframes in the videos. After that, Zhang Zhengyou's calibration method and Structure from Motion(SfM)are utilized to estimate the camera parameters. Next, Camera poses estimated from SfM were automatically calibrated using camera imaging trajectories as prior knowledge. Finally, Object-Based NeRF we proposed is utilized for the fine-scale reconstruction of plants. The OB-NeRF algorithm introduced a new ray sampling strategy that improved the efficiency and quality of target plant reconstruction without segmenting the background of images. Furthermore, the precision of the reconstruction was enhanced by optimizing camera poses. An exposure adjustment phase was integrated to improve the algorithm's robustness in uneven lighting conditions. The training process was significantly accelerated through the use of shallow MLP and multi-resolution hash encoding. Lastly, the camera imaging trajectories contributed to the automatic localization of target plants within the scene, enabling the automated extraction of Mesh.

Our pipeline reconstructed high-quality neural radiance fields of the target plant from captured videos in just 250 seconds, enabling the synthesis of novel viewpoint images and the extraction of Mesh. OB-NeRF surpasses NeRF in PSNR evaluation and reduces the reconstruction time from over 10 hours to just 30 Seconds. Compared to Instant-NGP, NeRFacto, and NeuS, OB-NeRF achieves higher reconstruction quality in a shorter reconstruction time. Moreover, Our reconstructed 3D model demonstrated superior texture and geometric fidelity compared to those generated by COLMAP and Kinect-based reconstruction methods. The $R^2$ was 0.9933,0.9881 and 0.9883 for plant height, leaf length, and leaf width, respectively. The MAE was 2.0947, 0.1898, and 0.1199 cm. The 3D reconstruction platform introduced in this study provides a robust foundation for high-throughput phenotyping and the creation of agricultural "digital twins".