69 citations · 72 across the 9 of their papers we have counts for
14 papers
R3PM-Net: Real-time, Robust, Real-world Point Matching Network
Yasaman Kashefbahrami, Erkut Akdag, Panagiotis Meletis +3
Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two point clouds. While deep-learning…
Training Semantic Segmentation on Heterogeneous Datasets
Panagiotis Meletis, Gijs Dubbelman
We explore semantic segmentation beyond the conventional, single-dataset homogeneous training and bring forward the problem of Heterogeneous Training of Semantic Segmentation (HTSS…
Towards holistic scene understanding: Semantic segmentation and beyond
Panagiotis Meletis
This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First,…
Deep Adaptive Multi-Intention Inverse Reinforcement Learning
Ariyan Bighashdel, Panagiotis Meletis, Pavol Jancura +1
This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn an a priori unknown number of nonlinear reward functions from unlabeled experts' demonstrat…
Merging Tasks for Video Panoptic Segmentation
Jake Rap, Panagiotis Meletis
In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed. Video panoptic segmentation (VPS) is a recently intr…
Part-aware Panoptic Segmentation
Daan de Geus, Panagiotis Meletis, Chenyang Lu +2
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifi…
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
Panagiotis Meletis, Xiaoxiao Wen, Chenyang Lu +2
In this technical report, we present two novel datasets for image scene understanding. Both datasets have annotations compatible with panoptic segmentation and additionally they ha…
Fast Panoptic Segmentation Network
Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally co…
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Panagiotis Meletis, Rob Romijnders, Gijs Dubbelman
Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose…
On Boosting Semantic Street Scene Segmentation with Weak Supervision
Panagiotis Meletis, Gijs Dubbelman
Training convolutional networks for semantic segmentation requires per-pixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work,…
Single Network Panoptic Segmentation for Street Scene Understanding
Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in…
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
Rob Romijnders, Panagiotis Meletis, Gijs Dubbelman
We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are p…
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network
Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
We present a single network method for panoptic segmentation. This method combines the predictions from a jointly trained semantic and instance segmentation network using heuristic…
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
Panagiotis Meletis, Gijs Dubbelman
We propose a convolutional network with hierarchical classifiers for per-pixel semantic segmentation, which is able to be trained on multiple, heterogeneous datasets and exploit th…