Search Results for author:
Found 8 papers, 5 papers with code
Mosaic-SDF for 3D Generative Models
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes.
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs).
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis.
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning.
Volume Rendering of Neural Implicit Surfaces
Accurate sampling is important to provide a precise coupling of geometry and radiance; and (iii) it allows efficient unsupervised disentanglement of shape and appearance in volume rendering.
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
In this work we address the challenging problem of multiview 3D surface reconstruction.
Implicit Geometric Regularization for Learning Shapes
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks.
Controlling Neural Level Sets
In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.
