6 papers
Differentiable Lifting for Topological Neural Networks
Jorge Luiz Franco, Gabriel Duarte, Alexander Nikitin +3
Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message-passing neural networks. In…
Towards Understanding 3D Vision: the Role of Gaussian Curvature
Sherlon Almeida da Silva, Davi Geiger, Luiz Velho +1
Recent advances in computer vision have predominantly relied on data-driven approaches that leverage deep learning and large-scale datasets. Deep neural networks have achieved rema…
Transformed Multi-view 3D Shape Features with Contrastive Learning
Márcus VinÃcius Lobo Costa, Sherlon Almeida da Silva, Bárbara Caroline Benato +2
This paper addresses the challenges in representation learning of 3D shape features by investigating state-of-the-art backbones paired with both contrastive supervised and self-sup…
The Role of Cyclopean-Eye in Stereo Vision
Sherlon Almeida da Silva, Davi Geiger, Luiz Velho +1
This work investigates the geometric foundations of modern stereo vision systems, with a focus on how 3D structure and human-inspired perception contribute to accurate depth recons…
Back to the Future Cyclopean Stereo: a human perception approach combining deep and geometric constraints
Sherlon Almeida da Silva, Davi Geiger, Luiz Velho +1
We innovate in stereo vision by explicitly providing analytical 3D surface models as viewed by a cyclopean eye model that incorporate depth discontinuities and occlusions. This geo…
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)
Emanuele Luzio, Moacir Antonelli Ponti
Anomaly detection is essential for identifying rare and significant events across diverse domains such as finance, cybersecurity, and network monitoring. This paper presents Synthe…