UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation

2026-03-13

AI Summary

This study addresses the challenge of acquiring accurate dense disparity maps in forestry environments, where intricate branch interlacing and complex canopy structures hinder supervised training of stereo matching networks essential for autonomous drone-based pruning. To overcome this limitation, we present the first high-fidelity synthetic binocular dataset tailored for forest depth estimation, built using Unreal Engine 5. Leveraging 115 high-resolution Quixel Megascans tree models, we simulate imagery matching the ZED Mini stereo camera (baseline: 63 mm, focal length: 2.8 mm) and generate 5,520 rectified stereo image pairs at 1920×1080 resolution across three pitch angles, each accompanied by pixel-accurate disparity ground truth. Combining geometric plausibility, photorealistic appearance, and large-scale precise annotations, this dataset fills a critical gap in supervised training data for forestry applications and will be publicly released to advance related research.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.

Problem

• forestry depth estimation
• dense ground-truth disparity
• UAV stereo vision
• occluded thin structures
• supervised stereo matching

Innovation

• synthetic dataset
• stereo matching
• photorealistic rendering
• UAV forestry
• depth estimation