3D Gaussian splatting improves remote sensing from few images
Remote Sensing Sparse-View 3D Gaussian Splatting via Depth Image-Based Rendering
Computer Vision and Pattern Recognition
Summary
Creating 3D views of a landscape or object from only a few photos is hard because there isn’t enough information to accurately guess the shapes and colors from all angles. The authors developed a new method that uses depth-based rendering to create extra virtual views, helping the system learn better 3D shapes and appearances. They also made sure the 3D parts don’t grow unrealistically tall, leading to more complete and accurate 3D models. Their method works well even when trained on just three images, outperforming previous techniques.
What this means in practice
- •For remote sensing analysts: Generate detailed 3D models of landscapes from very few satellite or aerial images to improve environmental monitoring.
- •For mapping software developers: Create more accurate geographic 3D visualizations when only sparse imagery data is available during map construction.
Authors
Jiaming Kang, Zhengxia Zou, Zhenwei Shi
Abstract
Remote sensing novel view synthesis under sparse observations remains challenging due to insufficient geometric constraints and limited cross-view supervision. Existing Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) methods are prone to overfitting and face challenges of depth ambiguities, missing cross-view information, and insufficient constraints in under-observed regions. To address these challenges, we propose DIBR-GS, a neural Gaussian Splatting framework that exploits Depth Image-Based Rendering (DIBR) to generate pseudo views for cross-view consistency supervision. Specifically, reliable geometric initialization is constructed by aligning monocular depth priors with sparse SfM reconstruction, and cross-view appearance priors are incorporated into neural Gaussian representations to enhance appearance modeling under sparse observations. Furthermore, we introduce a progressive DIBR-based pseudo-view supervision strategy to provide additional geometric and appearance constraints, enabling more complete reconstruction of weakly observed regions. In addition, a height-constrained anchor growth strategy is designed to suppress unreasonable Gaussian expansion. Experiments demonstrate that the proposed method achieves superior performance over existing approaches when training with only 3 input views. Compared with the previous best-performing method, it improves PSNR by 6.83 dB, with relative gains of 14\% in SSIM and 60\% in LPIPS, while maintaining competitive computational efficiency. Our code is available at https://github.com/kanehub/DIBR-GS