用频域分析选视角，少用图像也能高效重建3D场景
Frequency-based View Selection in Gaussian Splatting Reconstruction
- 通过频域分析预测新视角的信息增益
- 在仅需少量图像时达到当前最佳重建效果
- 适合需要高效3D重建的机器人感知应用
三维重建是机器人感知中的基础问题。本文研究主动视角选择问题，旨在用尽可能少的输入图像完成3D高斯点阵重建。尽管3D高斯点阵在图像渲染和三维重建方面已取得显著进展，但重建质量仍受2D图像选择及通过运动恢复结构（SfM）算法估计相机位姿的影响。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无法有效应对该问题，且难以泛化到新场景。本文通过在频域对潜在视角进行排序，无需真实数据即可有效估计新视角的潜在信息增益。该方法突破了现有模型架构与效能的限制，在视角选择任务上实现最先进性能，展现了其在高效基于图像的3D重建中的潜力。
原文摘要 · Abstract (English)
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.
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