From Canopy to Crown: High-Fidelity Tree Façade Synthesis from Nadir LiDAR data

Abstract. Synthesizing realistic façade views of individual trees from nadir-view remote sensing data would transform large-scale forest analysis, yet remains unsolved due to data scarcity and task ambiguity. We present the first conditional diffusion model to generate structurally plausible façade views of individual tree crowns from single nadir-view LiDAR rasters, leveraging the FOR-species20K benchmark dataset. Our approach integrates nadir projections with tree species and height within a U-Net-based denoising diffusion framework. Experiments demonstrate that nadir imagery alone is insufficient, but conditioning on species and height enables synthesis of visually realistic, species-specific façade views. The fully conditioned model achieves substantial gains in perceptual (LPIPS: 0.184) and structural (SSIM: 0.576) similarity, outperforming nadir-only baselines by more than twofold. Our results establish that ancillary attributes critically constrain the solution space, enabling diffusion models to infer plausible structures from ambiguous nadir input. This work demonstrates a scalable path to enriching nadir-based forest inventories with synthesized structural detail, reducing the need for resource-intensive ground surveys.