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Anestis Zaganidis

6 accepted papers

2025

Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning

Isma Hadji, Mehdi Noroozi, Victor Escorcia, Anestis Zaganidis, Brais Martinez, Georgios Tzimiropoulos
CVPR 2025poster

There has been immense progress recently in the visual quality of Stable Diffusion-based Super Resolution (SD-SR). However, deploying large diffusion models on computationally restricted devices such as mobile phones remains impractical due to the large model size and high latency. This is compounde…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

VladVA: Discriminative Fine-tuning of LVLMs

Yassine Ouali, Adrian Bulat, Alexandros Xenos, Anestis Zaganidis, Ioannis Maniadis Metaxas, Brais Martinez +1
CVPR 2025poster

Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models have limited language understanding, often exhibiting a "bag of words" behavior. At the same time, Large Vision-Language M…

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Anestis Zaganidis, Alexandros Zerntev, Tom Duckett, Grzegorz Cielniak
IROS 2019poster

Precise knowledge of pose is of great importance for reliable operation of mobile robots in outdoor environments. Simultaneous localization and mapping (SLAM) is the online construction of a map during exploration of an environment. One of the components of SLAM is loop closure detection, identifyin…

Cited by 50Source ↗Scholar ↗
2018

Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration

Anestis Zaganidis, Li Sun, Tom Duckett, Grzegorz Cielniak
RA-L 2018

Point cloud registration is the task of aligning 3D scans of the same environment captured from different poses. When semantic information is available for the points, it can be used as a prior in the search for correspondences to improve registration. Semantic-assisted Normal Distributions Transfor

Cited by 78Source ↗Scholar ↗
2018

Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data

Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, Tom Duckett
RA-L 2018

This letter presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term three-dimensional (3-D) Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3-D refinement of semantic maps (i.e. fusing semanti

Cited by 77Source ↗Scholar ↗
2017

Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure

Anestis Zaganidis, Martin Magnusson, Tom Duckett, Grzegorz Cielniak
IROS 2017poster

Point cloud registration is a core problem of many robotic applications, including simultaneous localization and mapping. The Normal Distributions Transform (NDT) is a method that fits a number of Gaussian distributions to the data points, and then uses this transform as an approximation of the real…

Cited by 54Source ↗Scholar ↗