Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform 3D Gaussian Splatting reconstructions with as few input images as possible. Although 3D Gaussian Splatting has made significant progress in image rendering and 3D reconstruction, the quality of the reconstruction is strongly impacted by the selection of 2D images and the estimation of camera poses through Structure-from-Motion (SfM) algorithms. Current methods to select views that rely on uncertainties from occlusions, depth ambiguities, or neural network predictions directly are insufficient to handle the issue and struggle to generalize to new scenes. By ranking the potential views in the frequency domain, we are able to effectively estimate the potential information gain of new viewpoints without ground truth data. By overcoming current constraints on model architecture and efficacy, our method achieves state-of-the-art results in view selection, demonstrating its potential for efficient image-based 3D reconstruction.
三维重建是机器人感知中的一个基础问题。我们研究了主动视角选择问题，旨在使用尽可能少的输入图像进行3D高斯分布（3D Gaussian Splatting）重建。尽管3D高斯分布在图像渲染和三维重建方面取得了显著进展，但重建质量在很大程度上受到2D图像选择以及通过结构光（Structure-from-Motion, SfM）算法估计相机姿态的影响。现有依赖遮挡、不确定性、深度模糊或神经网络预测的视角选择方法难以解决这一问题，且在泛化到新场景时表现不佳。通过在频域中对潜在视角进行排序，我们能够在没有真实数据的情况下有效估计新视角的潜在信息增益。通过克服当前模型架构和效果的限制，我们的方法在视角选择上实现了最新的成果，展现了其在高效基于图像的三维重建中的潜力。