no code implementations • 15 Feb 2024 • Theodora Kontogianni, Yuanwen Yue, Siyu Tang, Konrad Schindler
Our paper aims to initiate a paradigm shift, advocating for the adoption of continual learning methods through new experimental protocols that better emulate real-world conditions to facilitate breakthroughs in the field.
1 code implementation • 22 Dec 2023 • Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.
1 code implementation • 6 Jul 2023 • Binbin Xiang, Torben Peters, Theodora Kontogianni, Frawa Vetterli, Stefano Puliti, Rasmus Astrup, Konrad Schindler
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances.
no code implementations • 1 Jun 2023 • Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult, Francis Engelmann, Bastian Leibe, Konrad Schindler, Theodora Kontogianni
In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model.
1 code implementation • CVPR 2023 • Yuanwen Yue, Theodora Kontogianni, Konrad Schindler, Francis Engelmann
Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices.
1 code implementation • 14 Apr 2022 • Theodora Kontogianni, Ekin Celikkan, Siyu Tang, Konrad Schindler
We propose an interactive approach for 3D instance segmentation, where users can iteratively collaborate with a deep learning model to segment objects in a 3D point cloud directly.
1 code implementation • CVPR 2020 • Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe
That is, the convolutional kernel weights are mapped to the local surface of a given mesh.
no code implementations • ECCV 2020 • Theodora Kontogianni, Michael Gygli, Jasper Uijlings, Vittorio Ferrari
Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing.
Ranked #1 on Interactive Segmentation on DRIONS-DB
1 code implementation • 28 Jul 2019 • Francis Engelmann, Theodora Kontogianni, Bastian Leibe
In a thorough ablation study, we show that the receptive field size is directly related to the performance of 3D point cloud processing tasks, including semantic segmentation and object classification.
Ranked #43 on Semantic Segmentation on S3DIS Area5
no code implementations • 3 Apr 2019 • Cathrin Elich, Francis Engelmann, Theodora Kontogianni, Bastian Leibe
A lot of progress was made in the field of object classification and semantic segmentation.
Ranked #4 on 3D Semantic Instance Segmentation on ScanNetV2
3D Instance Segmentation 3D Semantic Instance Segmentation +4
no code implementations • 2 Oct 2018 • Francis Engelmann, Theodora Kontogianni, Jonas Schult, Bastian Leibe
In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds.
1 code implementation • 5 Feb 2018 • Francis Engelmann, Theodora Kontogianni, Alexander Hermans, Bastian Leibe
The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving encouraging segmentation results.
no code implementations • CVPR 2016 • Theodora Kontogianni, Markus Mathias, Bastian Leibe
Abstract In this paper, we address the problem of object discovery in time-varying, large-scale image collections.