Denoise-GS:用于Sparse-View 3D高斯式喷涂的自主监督Denoising
Yabo Xu1, Jin Ding1, Jianbin Zhang1
1College of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
Denoise-GS 增强了 3D 高斯斯普拉特,用于新的视图合成,特别是在稀疏,杂的数据中. 这种方法可以在具有挑战性的条件下提高图像质量和重建精度.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 图像处理 图像处理
背景情况:
- 三维高斯喷是新观点合成的领先技术.
- 像InstantSplat这样的现有方法在稀疏的视角,噪音和缺乏摄像头的前提下扎.
- 在稀疏输入场景中,发电质量的恶化是一个重要的问题.
研究的目的:
- 从稀疏和杂的输入数据中开发一个强大的高质量的新视图合成框架.
- 为了克服当前3D高斯喷方法在具有挑战性的现实世界条件中的局限性.
- 为了提高3D重建的准确性和视觉真实性.
主要方法:
- 提出了一个两轮优化框架,Denoise-GS.
- 集成Noise2Void (N2V) 用于自主监督的输入图像的无声化.
- 结合N2V-UNet与InstantSplat染,包括姿势分组和联合损失精细化.
主要成果:
- Denoise-GS显著提高了图像质量,实现了更高的峰值信号与噪声比率 (PSNR) 和结构相似度指数测量 (SSIM).
- 该方法在与主流方法相比,在新的视图生成中表现出优异的性能,输入图像稀疏且杂.
- 通过将高斯噪声添加到输入图像进行测试,成功模拟了杂的环境.
结论:
- Denoise-GS为3D重建和新视图合成提供了一种创新且实用的解决方案.
- 该框架有效地解决了稀疏和杂的输入数据所带来的挑战.
- 实现了最先进的结果,提高了3D高斯斯喷在现实世界的场景中的应用性.
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