Publications (16)
TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions
Or Perel, Hassan Abu Alhaija, Zian Wang +4
We introduce TRON, a rendering framework that combines 3D Gaussian ray tracing with neural rendering to enable realistic and controllable rendering of real-world 3D scenes under no…
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
Lior Yariv, Yoni Kasten, Dror Moran +4
In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry,…
ReMatching Dynamic Reconstruction Flow
Sara Oblak, Despoina Paschalidou, Sanja Fidler +1
Reconstructing a dynamic scene from image inputs is a fundamental computer vision task with many downstream applications. Despite recent advancements, existing approaches still str…
Frame Averaging for Equivariant Shape Space Learning
Matan Atzmon, Koki Nagano, Sanja Fidler +2
The task of shape space learning involves mapping a train set of shapes to and from a latent representation space with good generalization properties. Often, real-world collections…
Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields
Matan Atzmon, David Novotny, Andrea Vedaldi +1
Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code. So far, the…
Point Convolutional Neural Networks by Extension Operators
Matan Atzmon, Haggai Maron, Yaron Lipman
This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds. The framework consists of two operator…