arxiv_robotics 90% Match Research Paper Robotics Engineers,Computer Vision Researchers,Agricultural Scientists,Horticulturists 3 weeks ago

Temporal-Prior-Guided View Planning for Periodic 3D Plant Reconstruction

computer-vision › 3d-vision

📄 Abstract

Abstract: Periodic 3D reconstruction is essential for crop monitoring, but costly when each cycle restarts from scratch, wasting resources and ignoring information from previous captures. We propose temporal-prior-guided view planning for periodic plant reconstruction, in which a previously reconstructed model of the same plant is non-rigidly aligned to a new partial observation to form an approximation of the current geometry. To accommodate plant growth, we inflate this approximation and solve a set covering optimization problem to compute a minimal set of views. We integrated this method into a complete pipeline that acquires one additional next-best view before registration for robustness and then plans a globally shortest path to connect the planned set of views and outputs the best view sequence. Experiments on maize and tomato under hemisphere and sphere view spaces show that our system maintains or improves surface coverage while requiring fewer views and comparable movement cost compared to state-of-the-art baselines.

Key Contributions

Proposes a temporal-prior-guided view planning method for periodic 3D plant reconstruction that leverages previous scans. It uses non-rigid alignment to approximate current geometry, inflates it for growth accommodation, and solves a set covering problem for minimal views, followed by path planning for efficient data acquisition.

Business Value

Reduces the cost and improves the efficiency of 3D plant monitoring, enabling better crop management, yield prediction, and research in plant science.

Paper Metadata

Innovation Type

Algorithmic Approach for Efficient Reconstruction

Deployment Feasibility

Moderate. Requires robotic platforms (e.g., drones, mobile robots) equipped with sensors and the proposed planning software. Real-world plant growth variability can be challenging.

Limitations Addressed

Costly and inefficient periodic 3D reconstruction that restarts from scratch, ignoring prior information and wasting resources.

Performance Gains

Maintains or improves surface coverage while requiring fewer views and comparable movement costs compared to methods that restart reconstruction each cycle.

Technical Tags

View PlanningPeriodic Reconstruction3D Plant ReconstructionTemporal PriorNon-rigid AlignmentSet Covering OptimizationNext-Best-ViewPath PlanningCrop MonitoringMaizeTomato

Research Topics

Computer VisionRobotics3D ReconstructionAgricultural TechnologyOptimization

Methods & Architectures

Non-rigid RegistrationSet Covering OptimizationPath Planning AlgorithmsView Synthesis

Applications & Tasks

Agriculture Horticulture Plant Science Robotics Efficient 3D ReconstructionPeriodic ScanningView SelectionData Acquisition Optimization Periodic 3D Plant ReconstructionOptimizing View PlanningTracking Plant Growth

Datasets & Benchmarks

Datasets

Maize dataset, Tomato dataset

Surface CoverageNumber of ViewsMovement CostReconstruction Accuracy

Related Fields

RoboticsComputer GraphicsPhotogrammetryPrecision Agriculture

Keywords

3D reconstructionview planningplant monitoringagriculturetemporal priornon-rigid alignmentoptimizationroboticscrop monitoringnext-best-viewpath planning

Academic Context

#Computer Vision#Robotics#3D Reconstruction#Agricultural Technology#Optimization

Commercial Potential

Potential Products

Automated Plant Phenotyping Systems3D Crop Monitoring SoftwareRobotic Data Acquisition Solutions for Agriculture

Target Industries

AgricultureAgriTechBiotechnologyResearch Institutions

Use Case Examples

Monitoring plant growth and health in greenhousesAutomated phenotyping for crop breeding programsAssessing crop damage after environmental events

Competitive Edge

Offers a more efficient and data-aware approach to periodic 3D reconstruction by leveraging temporal priors, reducing the computational and time costs associated with repeated scans.

Market Opportunity

Growing market for precision agriculture and automated crop monitoring.

Revenue Models

Software licensingintegrated robotic systemsdata analysis services.

Resource Requirements

Compute Needs

Moderate for reconstruction and optimization, low for path planning.

Data Requirements

Requires sequences of 3D scans of the same plant over time.

Deployment Constraints

Requires robotic platforms, accurate sensor calibration, and robust non-rigid registration algorithms that can handle significant growth.

Scalability

Scalable to different plant types and reconstruction scenarios. Computational cost increases with plant complexity and growth rate.

Production Readiness

Maturity Level

Research Prototype

Time to Market

2-4 years for agricultural applications.

Patent Potential

Moderate for the view planning algorithm and reconstruction pipeline.

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