YiLing Yao

Yiling Yao, Wenjuan Zhang, Bing Zhang et al.

This study presents RSGaussian, an innovative novel view synthesis (NVS) method for aerial remote sensing scenes that incorporate LiDAR point cloud as constraints into the 3D Gaussian Splatting method, which ensures that Gaussians grow and split along geometric benchmarks, addressing the overgrowth and floaters issues occurs. Additionally, the approach introduces coordinate transformations with distortion parameters for camera models to achieve pixel-level alignment between LiDAR point clouds and 2D images, facilitating heterogeneous data fusion and achieving the high-precision geo-alignment required in aerial remote sensing. Depth and plane consistency losses are incorporated into the loss function to guide Gaussians towards real depth and plane representations, significantly improving depth estimation accuracy. Experimental results indicate that our approach has achieved novel view synthesis that balances photo-realistic visual quality and high-precision geometric estimation under aerial remote sensing datasets. Finally, we have also established and open-sourced a dense LiDAR point cloud dataset along with its corresponding aerial multi-view images, AIR-LONGYAN.

BoCheng Li, WenJuan Zhang, Bing Zhang et al.

3D Gaussian Splatting (3D GS) achieves impressive results in novel view synthesis for small, single-object scenes through Gaussian ellipsoid initialization and adaptive density control. However, when applied to large-scale remote sensing scenes, 3D GS faces challenges: the point clouds generated by Structure-from-Motion (SfM) are often sparse, and the inherent smoothing behavior of 3D GS leads to over-reconstruction in high-frequency regions, where have detailed textures and color variations. This results in the generation of large, opaque Gaussian ellipsoids that cause gradient artifacts. Moreover, the simultaneous optimization of both geometry and texture may lead to densification of Gaussian ellipsoids at incorrect geometric locations, resulting in artifacts in other views. To address these issues, we propose PSRGS, a progressive optimization scheme based on spectral residual maps. Specifically, we create a spectral residual significance map to separate low-frequency and high-frequency regions. In the low-frequency region, we apply depth-aware and depth-smooth losses to initialize the scene geometry with low threshold. For the high-frequency region, we use gradient features with higher threshold to split and clone ellipsoids, refining the scene. The sampling rate is determined by feature responses and gradient loss. Finally, we introduce a pre-trained network that jointly computes perceptual loss from multiple views, ensuring accurate restoration of high-frequency details in both Gaussian ellipsoids geometry and color. We conduct experiments on multiple datasets to assess the effectiveness of our method, which demonstrates competitive rendering quality, especially in recovering texture details in high-frequency regions.