NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications

2025-03-27

AI Summary

To address the fundamental incompatibility between dynamic object motion (e.g., rotating plants on a turntable) and conventional NeRF’s reliance on controlled camera orbiting in high-throughput plant phenotyping, this work proposes a novel NeRF-based point cloud reconstruction paradigm using a single static camera—integrating explicit object self-rotation modeling with pose-aware coordinate transformations. Methodologically, the framework leverages COLMAP for initial pose estimation, adaptive ROI cropping, explicit pose-conditioned coordinate mapping, and standard NeRF training—eliminating the need for complex multi-camera or robotic motion systems. This design drastically reduces hardware cost and deployment complexity, particularly benefiting expensive sensors such as hyperspectral cameras. Experiments yield a high-resolution 10-million-point cloud with an F-score of 99.98%, demonstrating exceptional geometric fidelity and photorealism. Moreover, both training and inference meet real-time requirements for industrial-scale phenotyping pipelines.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Machine Learning: Hardware-aware ML

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.

Problem

• Reconstructs point clouds with stationary camera for plant phenotyping
• Eliminates need for moving cameras in high-throughput environments
• Optimizes NeRF-based 3D reconstruction for indoor agricultural applications

Innovation

• Stationary camera captures rotating objects
• COLMAP-based pose estimation and transformation
• High-resolution point clouds with ROI focus