PLGS:鲁棒全景提升与3D高斯飞溅

IF 13.7
Yu Wang;Xiaobao Wei;Ming Lu;Guoliang Kang
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引用次数: 0

摘要

以前的方法利用神经辐射场(NeRF)进行全视提升,而他们的训练和渲染速度令人不满意。相比之下,3D高斯飞溅(3DGS)由于其快速的训练和渲染速度而成为一种突出的技术。然而,与NeRF不同的是,传统的3DGS可能不满足基本的平滑假设,因为它不依赖于任何参数化结构来渲染(例如mlp)。因此,传统的3DGS在本质上更容易受到有噪声的2D蒙版监督。在本文中,我们提出了一种名为PLGS的新方法,该方法使3DGS能够从嘈杂的2D分割蒙版中生成一致的全光分割蒙版,同时与基于nerf的方法相比,保持更高的效率。具体来说,我们建立了一个全光感知的结构化三维高斯模型来引入平滑性并设计有效的降噪策略。对于语义场,我们不是用运动结构初始化,而是构造可靠的语义锚点来初始化三维高斯函数。然后我们在训练期间使用这些锚点作为平滑正则化。此外,我们提出了一种使用伪标签的自训练方法,该方法通过将渲染蒙版与噪声蒙版合并来生成伪标签,以增强PLGS的鲁棒性。对于实例字段,我们将2D实例掩码投影到3D空间中,并将它们与定向边界框匹配,以生成跨视图一致的实例掩码以进行监督。在各种基准测试上的实验表明,我们的方法在分割质量和速度方面都优于以前的最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PLGS: Robust Panoptic Lifting With 3D Gaussian Splatting
Previous methods utilize the Neural Radiance Field (NeRF) for panoptic lifting, while their training and rendering speed are unsatisfactory. In contrast, 3D Gaussian Splatting (3DGS) has emerged as a prominent technique due to its rapid training and rendering speed. However, unlike NeRF, the conventional 3DGS may not satisfy the basic smoothness assumption as it does not rely on any parameterized structures to render (e.g., MLPs). Consequently, the conventional 3DGS is, in nature, more susceptible to noisy 2D mask supervision. In this paper, we propose a new method called PLGS that enables 3DGS to generate consistent panoptic segmentation masks from noisy 2D segmentation masks while maintaining superior efficiency compared to NeRF-based methods. Specifically, we build a panoptic-aware structured 3D Gaussian model to introduce smoothness and design effective noise reduction strategies. For the semantic field, instead of initialization with structure from motion, we construct reliable semantic anchor points to initialize the 3D Gaussians. We then use these anchor points as smooth regularization during training. Additionally, we present a self-training approach using pseudo labels generated by merging the rendered masks with the noisy masks to enhance the robustness of PLGS. For the instance field, we project the 2D instance masks into 3D space and match them with oriented bounding boxes to generate cross-view consistent instance masks for supervision. Experiments on various benchmarks demonstrate that our method outperforms previous state-of-the-art methods in terms of both segmentation quality and speed.
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