1 citations · 1 across the 5 of their papers we have counts for
5 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…
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…
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…
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…
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
Aleksandr Kim, Guillem Brasó, Aljoša Ošep +1
Most (3D) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships…