1 citations · 1 across the 4 of their papers we have counts for
6 papers
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
Aysim Toker, Marvin Eisenberger, Daniel Cremers +1
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques rem…
SeMoLi: What Moves Together Belongs Together
Jenny Seidenschwarz, Aljoša Ošep, Francesco Ferroni +2
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to p…
Better Call SAL: Towards Learning to Segment Anything in Lidar
Aljoša Ošep, Tim Meinhardt, Francesco Ferroni +3
We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engi…
The NeRFect Match: Exploring NeRF Features for Visual Localization
Qunjie Zhou, Maxim Maximov, Or Litany +1
In this work, we propose the use of Neural Radiance Fields (NeRF) as a scene representation for visual localization. Recently, NeRF has been employed to enhance pose regression and…
Lidar Panoptic Segmentation and Tracking without Bells and Whistles
Abhinav Agarwalla, Xuhua Huang, Jason Ziglar +5
State-of-the-art lidar panoptic segmentation (LPS) methods follow bottom-up segmentation-centric fashion wherein they build upon semantic segmentation networks by utilizing cluster…
Staged Contact-Aware Global Human Motion Forecasting
Luca Scofano, Alessio Sampieri, Elisabeth Schiele +3
Scene-aware global human motion forecasting is critical for manifold applications, including virtual reality, robotics, and sports. The task combines human trajectory and pose fore…