Distance estimation to objects and free-space boundaries in autonomous machine applications
Junghyun Kwon, San Jose, CA (US); Yilin Yang, Santa Clara, CA (US); Bala Siva Sashank Jujjavarapu, Sunnyvale, CA (US); Zhaoting Ye, Santa Clara, CA (US); Sangmin Oh, San Jose, CA (US); Minwoo Park, Saratoga, CA (US); and David Nister, Belleview, WA (US)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Jun. 20, 2023, as Appl. No. 18/337,854.
Application 18/337,854 is a continuation of application No. 17/449,310, filed on Sep. 29, 2021, granted, now 11,769,052.
Application 17/449,310 is a continuation of application No. 16/813,306, filed on Mar. 9, 2020, granted, now 11,170,299, issued on Nov. 9, 2021.
Application 16/813,306 is a continuation of application No. 16/728,595, filed on Dec. 27, 2019, granted, now 11,308,338, issued on Apr. 21, 2022.
Claims priority of provisional application 62/786,188, filed on Dec. 28, 2018.
Prior Publication US 2023/0334317 A1, Oct. 19, 2023
1. A method comprising: generating, using one or more machine learning models and based at least on sensor data obtained using one or more sensors of a machine, depth data that classifies one or more point as corresponding to one or more free-space locations within an environment that the machine is capable of navigating safely; and performing, based at least on the depth data, one or more operations associated with the machine.