ガウシアンスプラッティング再構成における周波数ベースの視点選択
Frequency-based View Selection in Gaussian Splatting Reconstruction
3Dガウシアンスプラッティングによる再構成で、周波数領域で視点をランク付けし、少ない入力画像で効率的に高品質な3D再構成を実現する視点選択手法を提案した。
著者: Monica M. Q. Li, Pierre-Yves Lajoie, Giovanni Beltrame
分類: cs.CV, cs.RO
原文アブストラクト
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.
