2019. 11. 1. The goal of image-based 3D reconstruction is to infer the 3D geometry and structure of objects and scenes from one or multiple 2D images.
2021. 9. 17. More precisely we can divide the conversion of 2D images to 3D model reconstruction into three generations. The first generation learns the 3D ...
Learning 3D shape. Single view supervision. Domain confusion. Adversarial learning. ABSTRACT. Learning to reconstruct 3D shapes using 2D images is an active
2022. 3. 2. In the proposed pipeline recognition and labeling of objects in 2D images deliver 2D segment silhouettes that are com- pared with the 2D ...
In this work we argue for the importance of a rich 3D scene model which can reason about object instances. A 2D image is a complex function of multiple attribu
for 3D object reconstruction when the projection loss is involved. generative model for 3D volumetric data and combined it with a 2D image embedding ...
Single-view 3D object reconstruction is a fundamental task in computer vision the conditional discriminator network combines a 2D CNN to extract image.
That is why the majority of extant works on using deep nets for 3D data resort to either volumetric grids or collections of images (2D views of the geometry).
1. Introduction. The choice of 3D representation plays a crucial role in 3D reconstruction problems from 2D images. Classical multi-.
[19] further extend such framework to capture the semantic part of object in 2D images. Pan et al. [28] im- prove the ability to generate complex shape by