Kavli Affiliate: Cheng Peng
| First 5 Authors: Yutao Tang, Yuxiang Guo, Deming Li, Cheng Peng,
| Summary:
Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve
photorealistic rendering; however, such capability is limited in sparse-view
scenarios due to sparse initialization and over-fitting floaters. Recent
progress in depth estimation and alignment can provide dense point cloud with
few views; however, the resulting pose accuracy is suboptimal. In this work, we
present SPARS3R, which combines the advantages of accurate pose estimation from
Structure-from-Motion and dense point cloud from depth estimation. To this end,
SPARS3R first performs a Global Fusion Alignment process that maps a prior
dense point cloud to a sparse point cloud from Structure-from-Motion based on
triangulated correspondences. RANSAC is applied during this process to
distinguish inliers and outliers. SPARS3R then performs a second, Semantic
Outlier Alignment step, which extracts semantically coherent regions around the
outliers and performs local alignment in these regions. Along with several
improvements in the evaluation process, we demonstrate that SPARS3R can achieve
photorealistic rendering with sparse images and significantly outperforms
existing approaches.
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