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

• URL: http://arxiv.org/abs/2603.15304v1
• Date: Fri, 13 Mar 2026 04:27:24 GMT
• Title: UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation
• Abstract summary: We present a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5)<n>One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes.<n> 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.
• Score: 5.266753902938501
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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