Christopher Ho

20210223401, Jul 22, 2021

Feb 2, 2021

17/165834

- Palo Alto CA, US
Christopher HO - San Francisco CA, US

G01S 17/931
G06T 7/136
G06T 7/187
G05D 1/02
G01S 17/89

Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).

20210157010, May 27, 2021

Feb 2, 2021

17/165817

- Palo Alto CA, US
Christopher HO - San Francisco CA, US

G01S 17/931
G06T 7/136
G06T 7/187
G05D 1/02
G01S 17/89

Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).

20190310378, Oct 10, 2019

Apr 4, 2019

16/375747

- Palo Alto CA, US
Christopher HO - San Francisco CA, US

G01S 17/93
G06T 7/136
G06T 7/187
G05D 1/02

Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).

20190146498, May 16, 2019

Feb 15, 2018

15/898222

- Gardena CA, US
Christopher Jung-Kit Ho - San Francisco CA, US

G05D 1/00
G05D 1/02
G06N 7/08
G05B 13/04
G06F 17/11
G06Q 10/04

Motion planning is described herein. Motion planning includes determining one or more trajectories and/or velocities. Trajectories and velocities are then provided to one or more controllers that cause a vehicle to travel to a location. By dynamically determining a motion path with various math equations, time may be saved by eliminating the need to choose between a plurality of motion plans.

20180240272, Aug 23, 2018

Feb 23, 2018

15/904097

- Tokyo, JP
Jun Murakawa - Foster City CA, US
Christopher Ho - Belmont CA, US

G06T 17/20
G06T 3/40

Systems, methods, and devices are disclosed for rendering computer graphics. In various embodiments, a displacement map is created for a plurality of surfaces and a tessellation process is initiated. It is determined that the tessellation density of a first set of surfaces and a second set of surfaces should be modified based on the displacement map. Based on the displacement map, a tessellation factor scale for each surface of the first set of surfaces is increased and a tessellation factor scale for each surface of the second set of surfaces is decreased, respectively.

20140347371, Nov 27, 2014

May 24, 2013

13/902661

- Tokyo, JP
Osman Steven - Foster City CA, US
Jun Murakawa - Foster City CA, US
Christopher Ho - Foster City CA, US

G06T 1/20

345501

A system has a central processing unit (CPU) and a graphics processing unit (GPU) that includes one or more registers. The GPU can change a resource descriptor in one of the GPU's registers. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

20140347375, Nov 27, 2014

May 24, 2013

13/902658

- Tokyo, JP
Christopher Ho - Foster City CA, US
Mark E. Cerny - Foster City CA, US
Steven Osman - Foster City CA, US
Jun Murakawa - Foster City CA, US
Dmitri Shtilman - Foster City CA, US
Jason Scanlin - Foster City CA, US

G06T 1/20

345522

A method for processing graphics for a GPU program, translating instructions from a shading language into an intermediate language with a front end of a GPU compiler; translating the instructions from the intermediate language into a GPU object language with a back end of the GPU compiler; wherein the instructions in the shading language include instructions defining a layout of resources for the GPU program.