用于稀疏视图合成的具有视图一致性的双目引导3D Gaussian Splatting
Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis
AI总结：
针对稀疏视图合成中外部神经先验噪声模糊的问题，提出基于双目立体一致性自监督和高斯不透明度约束的3D Gaussian Splatting方法，无需外部先验，并在多个数据集上优于现有方法。
摘要：
从稀疏输入进行新颖视图合成是3D计算机视觉中一项重要但具有挑战性的任务。以往方法探索将带有神经先验（例如深度先验）的3D Gaussian Splatting作为额外监督，与基于NeRF的方法相比，展现出可观的质量和效率。然而，来自2D预训练模型的神经先验通常存在噪声且模糊，难以精确指导辐射场的学习。本文提出一种使用Gaussian Splatting从稀疏视图合成新颖视图的新方法，该方法不需要外部先验作为监督。其核心思想在于探索由视差引导的图像变形所构建的每对双目图像之间固有的双目立体一致性自监督。为此，我们额外引入了高斯不透明度约束，用于规范高斯位置并避免高斯冗余，从而提高从稀疏视图推断3D高斯的鲁棒性和效率。在LLFF、DTU和Blender数据集上的大量实验表明，我们的方法显著优于最先进的方法。
英文摘要：
Novel view synthesis from sparse inputs is a vital yet challenging task in 3D computer vision. Previous methods explore 3D Gaussian Splatting with neural priors (e.g. depth priors) as an additional supervision, demonstrating promising quality and efficiency compared to the NeRF based methods. However, the neural priors from 2D pretrained models are often noisy and blurry, which struggle to precisely guide the learning of radiance fields. In this paper, We propose a novel method for synthesizing novel views from sparse views with Gaussian Splatting that does not require external prior as supervision. Our key idea lies in exploring the self-supervisions inherent in the binocular stereo consistency between each pair of binocular images constructed with disparity-guided image warping. To this end, we additionally introduce a Gaussian opacity constraint which regularizes the Gaussian locations and avoids Gaussian redundancy for improving the robustness and efficiency of inferring 3D Gaussians from sparse views. Extensive experiments on the LLFF, DTU, and Blender datasets demonstrate that our method significantly outperforms the state-of-the-art methods.
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