1 citations · 1 across the 6 of their papers we have counts for
6 papers
Towards Learning to Complete Anything in Lidar
Ayca Takmaz, Cristiano Saltori, Neehar Peri +4
We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contem…
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…
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…
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
Cristiano Saltori, Aljoša Ošep, Elisa Ricci +1
The ability to deploy robots that can operate safely in diverse environments is crucial for developing embodied intelligent agents. As a community, we have made tremendous progress…
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment
Mariia Gladkova, Nikita Korobov, Nikolaus Demmel +3
Direct methods have shown excellent performance in the applications of visual odometry and SLAM. In this work we propose to leverage their effectiveness for the task of 3D multi-ob…
Forecasting from LiDAR via Future Object Detection
Neehar Peri, Jonathon Luiten, Mengtian Li +3
Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, w…