1 citations · 1 across the 8 of their papers we have counts for
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DeTra: A Unified Model for Object Detection and Trajectory Forecasting
Sergio Casas, Ben Agro, Jiageng Mao +4
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading ma…
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Ben Agro, Quinlan Sykora, Sergio Casas +2
Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world -- t…
4D-Former: Multimodal 4D Panoptic Segmentation
Ali Athar, Enxu Li, Sergio Casas +1
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individua…
Towards Unsupervised Object Detection From LiDAR Point Clouds
Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong +4
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point c…
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
Enxu Li, Sergio Casas, Raquel Urtasun
Semantic segmentation of LiDAR point clouds has been widely studied in recent years, with most existing methods focusing on tackling this task using a single scan of the environmen…
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Anqi Joyce Yang, Sergio Casas, Nikita Dvornik +5
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labell…