top of page

News to help your R&D in artificial intelligence, machine learning, robotics, computer vision, smart hardware

As an Amazon Associate I earn

from qualifying purchases

Writer's picturemorrislee

Get 3D object shape from sparse views using epipolar features to guide diffusion model with Sparse3D

Get 3D object shape from sparse views using epipolar features to guide diffusion model with Sparse3D


Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views

arXiv paper abstract https://arxiv.org/abs/2308.14078



Reconstructing 3D objects from extremely sparse views is a long-standing and challenging problem ... image diffusion ... struggle to simultaneously achieve high-quality ... for ... novel-view synthesis (NVS) and geometry.


... present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs ... distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field.


... employ a controller that harnesses epipolar features from input views, guiding a pre-trained diffusion model, such as Stable Diffusion, to produce novel-view images that maintain 3D consistency with the input.


By tapping into 2D priors from powerful image diffusion models, ... integrated model consistently delivers high-quality results, even when faced with open-world objects.


To address the blurriness introduced by conventional SDS, ... introduce the category-score distillation sampling (C-SDS) to enhance detail.


... approach outperforms previous state-of-the-art works on the metrics regarding NVS and geometry reconstruction.



Please like and share this post if you enjoyed it using the buttons at the bottom!


Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact

Web site with my other posts by category https://morrislee1234.wixsite.com/website



27 views0 comments

コメント


ClickBank paid link

bottom of page