1 citations · 1 across the 6 of their papers we have counts for
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ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitives
Bartłomiej Baranowski, Stefano Esposito, Patricia Gschoßmann +2
3D Gaussian Splatting (3DGS) achieves state-of-the-art image quality and real-time performance in novel view synthesis but often suffers from a suboptimal spatial distribution of p…
TTT3R: 3D Reconstruction as Test-Time Training
Xingyu Chen, Yue Chen, Yuliang Xiu +2
Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly…
GenFusion: Closing the Loop between Reconstruction and Generation via Videos
Sibo Wu, Congrong Xu, Binbin Huang +2
Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap ca…
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
Xingyu Chen, Yue Chen, Yuliang Xiu +2
Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct super…
sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views
Eyvaz Najafli, Marius Kästingschäfer, Sebastian Bernhard +2
Reconstructing unbounded outdoor scenes from sparse outward-facing views poses significant challenges due to minimal view overlap. Previous methods often lack cross-scene understan…