While text-conditional 3D object generation and manipulation have seen r...
In this paper, we tackle the problem of generating a novel image from an...
In this paper, we focus on the problem of rendering novel views from a N...
3D semantic segmentation is a critical task in many real-world applicati...
Implicit Neural Representations (INRs) have emerged in the last few year...
Availability of labelled data is the major obstacle to the deployment of...
Estimating depth from images nowadays yields outstanding results, both i...
Point cloud classification is a popular task in 3D vision. However, prev...
We propose X-NeRF, a novel method to learn a Cross-Spectral scene
repres...
We address the problem of registering synchronized color (RGB) and
multi...
Embedding of large but redundant data, such as images or text, in a hier...
We present a novel high-resolution and challenging stereo dataset framin...
We introduce a novel architecture for neural disparity refinement aimed ...
Unsupervised Domain Adaptation (UDA) for point cloud classification is a...
Although recent semantic segmentation methods have made remarkable progr...
Although deep neural networks have achieved remarkable results for the t...
Defining and reliably finding a canonical orientation for 3D surfaces is...
Whole understanding of the surroundings is paramount to autonomous syste...
Object recognition in 3D point clouds is a challenging task, mainly when...
Establishing correspondences between 3D shapes is a fundamental task in ...
Matching surfaces is a challenging 3D Computer Vision problem typically
...
Recent works have proven that many relevant visual tasks are closely rel...