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Panoptic Segmentation of LiDAR Point Clouds


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Learning Kinematic Feasibility through Reinforcement Learning

We frame mobile manipulation as a reinforcement learning problem in which the base ensures kinematic feasibility of arbitrary end-effector motions. This results in a simple objective with which we can deploy the same approach across a variety of mobile robots, alleviating the need for expert-based adaptations. It also generalizes to unseen tasks at test time.

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Dynamic Object Removal & Inpainting

We address the problem of synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We propose the novel geometry-aware DynaFill architecture that follows a coarse-to-fine topology and incorporates our gated recurrent feedback mechanism. Our model achieves state-of-the-art performance compared to existing methods.

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Multi-Object Panoptic Tracking

We introduce and investigate a new perception task that we call MOPT which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking into a single coherent scene understanding problem. We present PanopticTrackNet and several new baselines to address this task using either LiDAR scans or images.

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Visual Localization in LiDAR Maps

We present novel CNN-based methods for monocular camera localization in LiDAR-maps that are independent of both the map and camera intrinsics. Our networks achieve state-of-the-art performance on KITTI, Argoverse, and Lyft Level5 while being the first deep learning methods to effectively generalize to unseen environments as well as to different sensors.

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Efficient Panoptic Segmentation

We present the novel EfficientPS architecture that consists of our shared backbone with 2-way FPN, followed by new instance and semantic segmentation heads, and our panoptic fusion module. Our network sets the new state-of-the-art on Cityscapes, KITTI, Mapillary Vistas and IDD while being the most efficient and fast panoptic segmentation model to date.

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