6 papers
Scene Grounding In the Wild
Tamir Cohen, Leo Segre, Shay Shomer-Chai +2
Reconstructing accurate 3D models of large-scale real-world scenes from unstructured, in-the-wild imagery remains a core challenge in computer vision, especially when the input vie…
Multi-View Foundation Models
Leo Segre, Or Hirschorn, Shai Avidan
Foundation models are vital tools in various Computer Vision applications. They take as input a single RGB image and output a deep feature representation that is useful for various…
Frequency-Aware Gaussian Splatting Decomposition
Yishai Lavi, Leo Segre, Shai Avidan
3D Gaussian Splatting (3D-GS) enables efficient novel view synthesis, but treats all frequencies uniformly, making it difficult to separate coarse structure from fine detail. Recen…
Optimize the Unseen -- Fast NeRF Cleanup with Free Space Prior
Leo Segre, Shai Avidan
Neural Radiance Fields (NeRF) have advanced photorealistic novel view synthesis, but their reliance on photometric reconstruction introduces artifacts, commonly known as "floaters"…
VF-NeRF: Viewshed Fields for Rigid NeRF Registration
Leo Segre, Shai Avidan
3D scene registration is a fundamental problem in computer vision that seeks the best 6-DoF alignment between two scenes. This problem was extensively investigated in the case of p…
Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation maps
Leo Segre, Or Hirschorn, Dvir Ginzburg +1
Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very differ…