Sam Schofield
According to our database1,
Sam Schofield authored at least 15 papers
between 2024 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning.
CoRR, May, 2026
CoRR, April, 2026
Real-Time Branch-to-Tool Distance Estimation for Autonomous UAV Pruning: Benchmarking Five DEFOM-Stereo Variants from Simulation to Jetson Deployment.
CoRR, March, 2026
UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation.
CoRR, March, 2026
Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications.
CoRR, February, 2026
Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications.
CoRR, February, 2026
Towards Gold-Standard Depth Estimation for Tree Branches in UAV Forestry: Benchmarking Deep Stereo Matching Methods.
CoRR, January, 2026
2025
Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems.
CoRR, December, 2025
YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications.
CoRR, December, 2025
Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images.
CoRR, December, 2025
Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications.
CoRR, December, 2025
2024
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration.
CoRR, 2024
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models.
CoRR, 2024
Proceedings of the 39th International Conference on Image and Vision Computing New Zealand, 2024
Deep Learning-Based Depth Map Generation and YOLO-Integrated Distance Estimation for Radiata Pine Branch Detection Using Drone Stereo Vision.
Proceedings of the 39th International Conference on Image and Vision Computing New Zealand, 2024