AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

How to

Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.

Show all

2024

Federico, G.; Carrara, F.; Amato, G.; Benedetto, M. Di

Spatio-Temporal 3D Reconstruction from Frame Sequences and Feature Points Proceedings Article

In: ACM Int. Conf. Proc. Ser., pp. 52–64, Association for Computing Machinery, 2024, ISBN: 979-840071794-9 (ISBN).

Abstract | Links | BibTeX | Tags: 3D reconstruction, Adversarial machine learning, Artificial intelligence, Color motion pictures, Color photography, Contrastive Learning, De-noising, Deep learning, Denoising Diffusion Probabilistic Model, Frame features, machine learning, Machine-learning, Probabilistic models, Signed Distance Field, Signed distance fields, Spatio-temporal, Video Reconstruction, Video streaming

@inproceedings{federico_spatio-temporal_2024,

title = {Spatio-Temporal 3D Reconstruction from Frame Sequences and Feature Points},

author = {G. Federico and F. Carrara and G. Amato and M. Di Benedetto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203128613&doi=10.1145%2f3672406.3672415&partnerID=40&md5=2a0dc51baa15f0dcd7f9d2cca708ec15},

doi = {10.1145/3672406.3672415},

isbn = {979-840071794-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {52–64},

publisher = {Association for Computing Machinery},

abstract = {Reconstructing a large real environment is a fundamental task to promote eXtended Reality adoption in industrial and entertainment fields. However, the short range of depth cameras, the sparsity of LiDAR sensors, and the huge computational cost of Structure-from-Motion pipelines prevent scene replication in near real time. To overcome these limitations, we introduce a spatio-temporal diffusion neural architecture, a generative AI technique that fuses temporal information (i.e., a short temporally-ordered list of color photographs, like sparse frames of a video stream) with an approximate spatial resemblance of the explored environment. Our aim is to modify an existing 3D diffusion neural model to produce a Signed Distance Field volume from which a 3D mesh representation can be extracted. Our results show that the hallucination approach of diffusion models is an effective methodology where a fast reconstruction is a crucial target. © 2024 Owner/Author.},

keywords = {3D reconstruction, Adversarial machine learning, Artificial intelligence, Color motion pictures, Color photography, Contrastive Learning, De-noising, Deep learning, Denoising Diffusion Probabilistic Model, Frame features, machine learning, Machine-learning, Probabilistic models, Signed Distance Field, Signed distance fields, Spatio-temporal, Video Reconstruction, Video streaming},

pubstate = {published},

tppubtype = {inproceedings}

}