19 citations · 19 across the 12 of their papers we have counts for
7 papers · 1 filter
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers +1
The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious sel…
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
Vladimir Yugay, Yue Li, Theo Gevers +1
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation. Our approach enables interactive-time reconstruction and ph…
Auto-Vocabulary Semantic Segmentation
Osman Ülger, Maksymilian Kulicki, Yuki Asano +1
Open-Vocabulary Segmentation (OVS) methods are capable of performing semantic segmentation without relying on a fixed vocabulary, and in some cases, without training or fine-tuning…
Union-over-Intersections: Object Detection beyond Winner-Takes-All
Aritra Bhowmik, Pascal Mettes, Martin R. Oswald +1
This paper revisits the problem of predicting box locations in object detection architectures. Typically, each box proposal or box query aims to directly maximize the intersection-…
Relational Prior Knowledge Graphs for Detection and Instance Segmentation
Osman Ülger, Yu Wang, Ysbrand Galama +3
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects. In this paper, we investigate the effectiven…
SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation
Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors. Despite considerable progress in removing hand-crafted…
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
Weijie Wei, Martin R. Oswald, Fatemeh Karimi Nejadasl +1
In this paper, we focus on semantic segmentation method for point clouds of urban scenes. Our fundamental concept revolves around the collaborative utilization of diverse scene rep…