205 citations · 327 across the 10 of their papers we have counts for
13 papers
Interactive4D: Interactive 4D LiDAR Segmentation
Ilya Fradlin, Idil Esen Zulfikar, Kadir Yilmaz +2
Interactive segmentation has an important role in facilitating the annotation process of future LiDAR datasets. Existing approaches sequentially segment individual objects at each…
Is Continual Learning Ready for Real-world Challenges?
Theodora Kontogianni, Yuanwen Yue, Siyu Tang +1
Despite continual learning's long and well-established academic history, its application in real-world scenarios remains rather limited. This paper contends that this gap is attrib…
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni +4
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver…
Towards accurate instance segmentation in large-scale LiDAR point clouds
Binbin Xiang, Torben Peters, Theodora Kontogianni +4
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…
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult +4
During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point t…
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
Yuanwen Yue, Theodora Kontogianni, Konrad Schindler +1
We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruct…
Interactive Object Segmentation in 3D Point Clouds
Theodora Kontogianni, Ekin Celikkan, Siyu Tang +1
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.…
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes
Jonas Schult, Francis Engelmann, Theodora Kontogianni +1
We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that combines two types of convolutions. The first type, geodesic…
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Theodora Kontogianni, Michael Gygli, Jasper Uijlings +1
In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an…
Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
Francis Engelmann, Theodora Kontogianni, Bastian Leibe
In this work, we propose Dilated Point Convolutions (DPC). In a thorough ablation study, we show that the receptive field size is directly related to the performance of 3D point cl…
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Cathrin Elich, Francis Engelmann, Theodora Kontogianni +1
Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of obj…
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
Francis Engelmann, Theodora Kontogianni, Jonas Schult +1
In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce…
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Francis Engelmann, Theodora Kontogianni, Alexander Hermans +1
Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space…