FastPro-Gaussian: Accelerated True Digital Orthophoto Map Generation with Progressive Densification and Spherical-to-Ellipsoidal Gaussian Transformation
TL;DR
Compared to state-of-the-art methods, the proposed FastPro-Gaussian achieves comparable TDOM fidelity with over two-fold acceleration in training time (notably more than two times faster than Tortho-GS), confirming its strong potential for practical deployment in geospatial production pipelines.
Abstract
True digital orthophoto maps (TDOMs) serve as foundational geospatial products for applications in land surveying, urban planning, and emergency management. Conventional TDOM generation relies on differential correction, often resulting in cartographic artifacts such as geometric discontinuities, radiometric inconsistencies, and linear feature misalignments. Although recent methods leverage 3D Gaussian splatting to bypass differential correction, their computational demands hinder real-world deployment. To address these limitations, we propose FastPro-Gaussian, a novel framework enabling rapid high-quality TDOM generation on consumer-grade GPUs. Our contributions are threefold: block-based processing ensuring scalability for large-scale scenes; progressive densification stabilizing model optimization and reducing training iterations; and spherical-to-ellipsoidal Gaussian transformation, accelerating early-stage optimization of structural features (e.g., building edges). Experiments demonstrate that FastPro-Gaussian surpasses commercial solutions (ContextCapture, Metashape, and Pix4Dmapper) in rendering quality for building facades, edges, and roads. Compared to state-of-the-art methods, it achieves comparable TDOM fidelity with over two-fold acceleration in training time (notably more than two times faster than Tortho-GS). These gains in efficiency and efficacy confirm its strong potential for practical deployment in geospatial production pipelines.