In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting framework. It primarily targets two aspects: optimization of the partitioning strategy and enhancement of adaptive density control. Specifically, an adaptive partitioning strategy guided by scene complexity is designed to ensure more balanced computational workloads across spatial blocks. To preserve scene integrity, auxiliary point clouds are integrated during partition optimization. Furthermore, a pixel weight-scaling mechanism is employed to regulate the average gradient in adaptive density control, thereby mitigating excessive densification of Gaussians. This design accelerates the training process while maintaining high-fidelity rendering quality. Additionally, a task-scheduling algorithm based on frequency-domain analysis is incorporated to further improve computational resource utilization. Extensive experiments on multiple large-scale UAV datasets demonstrate that the proposed framework can be trained efficiently on a single RTX 3090 GPU, achieving more than a 50% reduction in average optimization time while maintaining PSNR, SSIM and LPIPS values that are comparable to or better than representative 3DGS-based methods; on the MatrixCity-S dataset (>6000 images), it attains the highest PSNR among 3DGS-based approaches and completes training on a single 24 GB GPU in less than 60% of the training time of DOGS. Nevertheless, the current framework still requires several hours of optimization for city-scale scenes and has so far only been evaluated on static UAV imagery with a fixed camera model, which may limit its applicability to dynamic scenes or heterogeneous sensor configurations.

A novel deep learning architecture called Attention-Residual block-Embedded Adversarial Networks (AREANs) is proposed in this letter, which can give the robust pixel-wise scene understanding in remote sensing images without any post-processing and additional data. The generator of AREANs, a novel designed encoder-decoder structure network, takes full advantage of Attention-Residual block to learn local-to-global contextual information through semantic and position information enhanced aggregation. To further improve the performance, a patchGAN-based discriminator is applied to train the generator. This training method can not only promote to mine the distinguishable and inherent features from data, but also boost the feature extraction performance of the generator through fine-tuning its parameters. Moreover, the multipath composite loss is proposed as an auxiliary loss in the generator training stage to cope with the class imbalance problem. The comparative experimental results demonstrate that our proposed AREANs can achieve better performance on both Vaihingen and Potsdam datasets.

Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Although 3D Gaussian Splatting (3DGS) offers an efficient explicit scene representation, its use in large-scale UAV orthophoto generation is limited by high memory consumption, unstable densification, and insufficient support for mapping-oriented orthographic rendering. This paper proposes a single-GPU 3DGS framework for UAV orthophoto generation by integrating adaptive spatial block partitioning, deterministic structure-sensitive adaptive density control, and core–buffer tiled orthographic rendering with weighted blending. The proposed framework decomposes large scenes into resource-bounded subregions, guides Gaussian densification using fixed multi-view neighborhoods and edge-enhanced dynamic consistency, and generates large-format orthophotos with reduced boundary and seam artifacts. Experiments on MatrixCity-S and multiple UAV photogrammetric datasets show that the method achieves competitive reconstruction quality and improved resource efficiency. On MatrixCity-S, it reaches 29.01 dB PSNR and 0.901 SSIM, while completing training in 1 h 49 min on a single NVIDIA RTX 3090 GPU. Compared with BlockGS, peak VRAM consumption is reduced by more than 38% across datasets. Under geo-aligned comparison conditions, line-measurement comparisons with MetaShape and Pix4DMapper yield RMSE values of 0.099 m and 0.087 m, respectively. These results demonstrate the potential of the proposed framework for memory-efficient 3DGS-based UAV orthophoto generation under constrained hardware resources, while further control-point-based validation is still needed for rigorous surveying-grade applications.

Keywords: Bundle Adjustment, Parallax Angle, Convergence, Aerial Photogrammetry, Nonlinear Optimization As the basis for photogrammetry, Bundle Adjustment (BA) can restore the pose of cameras accurately, reconstruct the 3D models of environment, and serve as the criterion of digital production. For the classical nonlinear optimization of BA model based on the Euclidean coordinate, it suffers the problem of being seriously dependent on the initial values, making it unable to converge fast or converge to a global minimum. This paper first introduces a new BA model based on parallax angle feature parametrization, and then analyses the applications and performance of the model used in photogrammetry field. To discuss the impact and the performance of the model (especially in aerial photogrammetry), experiments using two aerial datasets under different initial values were conducted. The experiment results are better than some well-known software packages of BA, and the simulation results illustrate the stability of the new model than the normal BA under the Euclidean coordinate. In all, the new BA model shows promising applications in faster and more efficient aerial photogrammetry with good convergence and fast convergence speed.