US 12,437,412 B2
Deep neural network for segmentation of road scenes and animate object instances for autonomous driving applications
Ke Chen, Sunnvale, CA (US); Nikolai Smolyanskiy, Seattle, WA (US); Alexey Kamenev, Bellevue, WA (US); Ryan Oldja, Redmond, WA (US); Tilman Wekel, Sunnyvale, CA (US); David Nister, Bellevue, WA (US); Joachim Pehserl, Lynnwood, WA (US); Ibrahim Eden, Redmond, WA (US); Sangmin Oh, San Jose, CA (US); and Ruchi Bhargava, Redmond, WA (US)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Dec. 27, 2023, as Appl. No. 18/397,921.
Application 18/397,921 is a continuation of application No. 16/938,706, filed on Jul. 24, 2020, granted, now 12,051,206.
Claims priority of provisional application 62/878,659, filed on Jul. 25, 2019.
Prior Publication US 2025/0014186 A1, Jan. 9, 2025
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G05D 1/00 (2006.01); G05D 1/81 (2024.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06T 5/50 (2006.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06V 10/44 (2022.01)
CPC G06T 7/11 (2017.01) [G05D 1/0088 (2013.01); G05D 1/81 (2024.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06T 5/50 (2013.01); G06T 7/10 (2017.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01); G06V 10/454 (2022.01)] 20 Claims
OG exemplary drawing
 
1. One or more processors comprising processing circuitry to:
generate, using a neural network and based at least on a representation of image data corresponding to an environment of an ego-object, one or more classifications of one or more pixels, the one or more classifications indicating associations of the one or more pixels with one or more unique instances corresponding to one or more respective channels of the neural network;
generate, based at least on the one or more classifications, one or more bounding shapes of the one or more unique instances of one or more detected objects in the environment; and
execute one or more operations of the ego-object based at least on the one or more bounding shapes.