3 citations · 3 across the 4 of their papers we have counts for
4 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…
NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation
Tim Meinhardt, Matt Feiszli, Yuchen Fan +2
Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing.…
DeVIS: Making Deformable Transformers Work for Video Instance Segmentation
Adrià Caelles, Tim Meinhardt, Guillem Brasó +1
Video Instance Segmentation (VIS) jointly tackles multi-object detection, tracking, and segmentation in video sequences. In the past, VIS methods mirrored the fragmentation of thes…