588 research outputs found
Deep Distance Sensitivity Oracles
One of the most fundamental graph problems is finding a shortest path from a
source to a target node. While in its basic forms the problem has been studied
extensively and efficient algorithms are known, it becomes significantly harder
as soon as parts of the graph are susceptible to failure. Although one can
recompute a shortest replacement path after every outage, this is rather
inefficient both in time and/or storage. One way to overcome this problem is to
shift computational burden from the queries into a pre-processing step, where a
data structure is computed that allows for fast querying of replacement paths,
typically referred to as a Distance Sensitivity Oracle (DSO). While DSOs have
been extensively studied in the theoretical computer science community, to the
best of our knowledge this is the first work to construct DSOs using deep
learning techniques. We show how to use deep learning to utilize a
combinatorial structure of replacement paths. More specifically, we utilize the
combinatorial structure of replacement paths as a concatenation of shortest
paths and use deep learning to find the pivot nodes for stitching shortest
paths into replacement paths.Comment: arXiv admin note: text overlap with arXiv:2007.11495 by other author
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Closed-set 3D perception models trained on only a pre-defined set of object
categories can be inadequate for safety critical applications such as
autonomous driving where new object types can be encountered after deployment.
In this paper, we present a multi-modal auto labeling pipeline capable of
generating amodal 3D bounding boxes and tracklets for training models on
open-set categories without 3D human labels. Our pipeline exploits motion cues
inherent in point cloud sequences in combination with the freely available 2D
image-text pairs to identify and track all traffic participants. Compared to
the recent studies in this domain, which can only provide class-agnostic auto
labels limited to moving objects, our method can handle both static and moving
objects in the unsupervised manner and is able to output open-vocabulary
semantic labels thanks to the proposed vision-language knowledge distillation.
Experiments on the Waymo Open Dataset show that our approach outperforms the
prior work by significant margins on various unsupervised 3D perception tasks.Comment: ICCV 202
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