Intern-GS：视觉模型引导的稀疏视角三维高斯泼溅
Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting
AI总结：
Intern-GS利用视觉基础模型引导三维高斯泼溅的初始化与优化，解决稀疏视角重建难题，在LLFF、DTU等数据集上取得最先进渲染质量。
摘要：
稀疏视角场景重建常因观测数据有限而面临重大挑战。这些限制导致信息不完整，使得现有方法的重建效果欠佳。为解决这一问题，我们提出Intern-GS，一种新颖的方法，有效利用视觉基础模型的丰富先验知识来增强稀疏视角高斯泼溅过程，从而实现高质量场景重建。具体而言，Intern-GS利用视觉基础模型同时引导三维高斯泼溅的初始化和优化过程，有效应对稀疏输入的局限性。在初始化过程中，我们的方法采用DUSt3R生成密集且无冗余的高斯点云。这一方法显著缓解了传统运动恢复结构（SfM）方法在稀疏视角约束下常遇到的困难。在优化过程中，视觉基础模型为未观测视角预测深度和外观，通过细化三维高斯来补偿未见区域缺失的信息。大量实验表明，Intern-GS在多种数据集（包括前向场景和大型场景，如LLFF、DTU和Tanks and Temples）上均达到了最先进的渲染质量。
英文摘要：
Sparse-view scene reconstruction often faces significant challenges due to the constraints imposed by limited observational data. These limitations result in incomplete information, leading to suboptimal reconstructions using existing methodologies. To address this, we present Intern-GS, a novel approach that effectively leverages rich prior knowledge from vision foundation models to enhance the process of sparse-view Gaussian Splatting, thereby enabling high-quality scene reconstruction. Specifically, Intern-GS utilizes vision foundation models to guide both the initialization and the optimization process of 3D Gaussian splatting, effectively addressing the limitations of sparse inputs. In the initialization process, our method employs DUSt3R to generate a dense and non-redundant gaussian point cloud. This approach significantly alleviates the limitations encountered by traditional structure-from-motion (SfM) methods, which often struggle under sparse-view constraints. During the optimization process, vision foundation models predict depth and appearance for unobserved views, refining the 3D Gaussians to compensate for missing information in unseen regions. Extensive experiments demonstrate that Intern-GS achieves state-of-the-art rendering quality across diverse datasets, including both forward-facing and large-scale scenes, such as LLFF, DTU, and Tanks and Temples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
