5 citations · 5 across the 7 of their papers we have counts for
8 papers
Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles
NVIDIA, :, Sérgio Agostinho +25
We introduce Foundation4D, a unified feed-forward model for geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi-camera driving video. A sh…
Beyond the Image Plane: World-Grounded Queries for Multi-Object Tracking
Orcun Cetintas, Guillem Brasó, Tim Meinhardt +1
Monocular videos record 3D scenes as sequences of 2D image-plane projections, obscuring depth and spatial relationships. Multi-object trackers localize and associate objects primar…
NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking
Benjamin Missaoui, Orcun Cetintas, Guillem Brasó +2
The long-standing division between \textit{online} and \textit{offline} Multi-Object Tracking (MOT) has led to fragmented solutions that fail to address the flexible temporal requi…
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…
Zero-Shot 4D Lidar Panoptic Segmentation
Yushan Zhang, Aljoša Ošep, Laura Leal-Taixé +1
Zero-shot 4D segmentation and recognition of arbitrary objects in Lidar is crucial for embodied navigation, with applications ranging from streaming perception to semantic mapping…
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
Yizhou Wang, Tim Meinhardt, Orcun Cetintas +6
Object perception from multi-view cameras is crucial for intelligent systems, particularly in indoor environments, e.g., warehouses, retail stores, and hospitals. Most traditional…
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Orcun Cetintas, Tim Meinhardt, Guillem Brasó +1
Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-sca…
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…