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pro vyhledávání: '"Lambert,John"'
Autor:
Lambert, John, Li, Yuguang, Boyadzhiev, Ivaylo, Wixson, Lambert, Narayana, Manjunath, Hutchcroft, Will, Hays, James, Dellaert, Frank, Kang, Sing Bing
We propose a new system for automatic 2D floorplan reconstruction that is enabled by SALVe, our novel pairwise learned alignment verifier. The inputs to our system are sparsely located 360$^\circ$ panoramas, whose semantic features (windows, doors, a
Externí odkaz:
http://arxiv.org/abs/2406.19390
Autor:
Montali, Nico, Lambert, John, Mougin, Paul, Kuefler, Alex, Rhinehart, Nick, Li, Michelle, Gulino, Cole, Emrich, Tristan, Yang, Zoey, Whiteson, Shimon, White, Brandyn, Anguelov, Dragomir
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open Sim Agents Challenge (WOSAC). WOSAC is the first public challenge to tackle this task and propo
Externí odkaz:
http://arxiv.org/abs/2305.12032
Autor:
Wilson, Benjamin, Qi, William, Agarwal, Tanmay, Lambert, John, Singh, Jagjeet, Khandelwal, Siddhesh, Pan, Bowen, Kumar, Ratnesh, Hartnett, Andrew, Pontes, Jhony Kaesemodel, Ramanan, Deva, Carr, Peter, Hays, James
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from se
Externí odkaz:
http://arxiv.org/abs/2301.00493
Autor:
Lambert, John, Hays, James
High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verif
Externí odkaz:
http://arxiv.org/abs/2212.07312
We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxon
Externí odkaz:
http://arxiv.org/abs/2112.13762
Autor:
Kumar, Ram Shankar Siva, Nyström, Magnus, Lambert, John, Marshall, Andrew, Goertzel, Mario, Comissoneru, Andi, Swann, Matt, Xia, Sharon
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the int
Externí odkaz:
http://arxiv.org/abs/2002.05646
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