no code implementations • 18 Mar 2024 • Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian vasile, Jeremie Mary, Andrew Comport, Valérie Gouet-Brunet
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
no code implementations • 31 Jul 2023 • Cheng-You Lu, Peisen Zhou, Angela Xing, Chandradeep Pokhariya, Arnab Dey, Ishaan Shah, Rugved Mavidipalli, Dylan Hu, Andrew Comport, Kefan Chen, Srinath Sridhar
Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes.
no code implementations • 2 Mar 2023 • Houssem Boulahbal, Adrian Voicila, Andrew Comport
Apart from the transformer architecture, one of the main contributions with respect to prior works lies in the objective function that enforces spatio-temporal consistency across a sequence of output frames rather than a single output frame.
no code implementations • 15 Jun 2022 • Houssem Boulahbal, Adrian Voicila, Andrew Comport
This paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion.
no code implementations • 2 Mar 2022 • Houssem Boulahbal, Adrian Voicila, Andrew Comport
One novelty of the proposed method is the use of the multi-head attention of the transformer network that matches moving objects across time and models their interaction and dynamics.
no code implementations • 28 Jun 2021 • Houssem eddine Boulahbal, Adrian Voicila, Andrew Comport
This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture.