4 citations · 4 across the 5 of their papers we have counts for
5 papers
Informative Rays Selection for Few-Shot Neural Radiance Fields
Marco Orsingher, Anthony Dell'Eva, Paolo Zani +2
Neural Radiance Fields (NeRF) have recently emerged as a powerful method for image-based 3D reconstruction, but the lengthy per-scene optimization limits their practical usage, esp…
Learning Neural Radiance Fields from Multi-View Geometry
Marco Orsingher, Paolo Zani, Paolo Medici +1
We present a framework, called MVG-NeRF, that combines classical Multi-View Geometry algorithms and Neural Radiance Fields (NeRF) for image-based 3D reconstruction. NeRF has revolu…
Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians
Anthony Dell'Eva, Marco Orsingher, Massimo Bertozzi
Generating dense point clouds from sparse raw data benefits downstream 3D understanding tasks, but existing models are limited to a fixed upsampling ratio or to a short range of in…
Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction
Marco Orsingher, Paolo Zani, Paolo Medici +1
In this paper, a complete pipeline for image-based 3D reconstruction of urban scenarios is proposed, based on PatchMatch Multi-View Stereo (MVS). Input images are firstly fed into…
Efficient View Clustering and Selection for City-Scale 3D Reconstruction
Marco Orsingher, Paolo Zani, Paolo Medici +1
Image datasets have been steadily growing in size, harming the feasibility and efficiency of large-scale 3D reconstruction methods. In this paper, a novel approach for scaling Mult…