公开(公告)号：US20200167943A1

公开(公告)日：2020-05-28

申请号：US16565885

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Kihwan Kim ,  Jinwei Gu ,  Chen Liu ,  Jan Kautz

IPC: G06T7/593 ,  G06T7/11 ,  G06T7/143 ,  G06N3/04

Abstract: Planar regions in three-dimensional scenes offer important geometric cues in a variety of three-dimensional perception tasks such as scene understanding, scene reconstruction, and robot navigation. Image analysis to detect planar regions can be performed by a deep learning architecture that includes a number of neural networks configured to estimate parameters for the planar regions. The neural networks process an image to detect an arbitrary number of plane objects in the image. Each plane object is associated with a number of estimated parameters including bounding box parameters, plane normal parameters, and a segmentation mask. Global parameters for the image, including a depth map, can also be estimated by one of the neural networks. Then, a segmentation refinement network jointly optimizes (i.e., refines) the segmentation masks for each instance of the plane objects and combines the refined segmentation masks to generate an aggregate segmentation mask for the image.

公开(公告)号：US11037051B2

公开(公告)日：2021-06-15

申请号：US16565885

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Kihwan Kim ,  Jinwei Gu ,  Chen Liu ,  Jan Kautz

IPC: G06N3/04 ,  G06T7/11 ,  G06T7/50 ,  G06K9/62

Abstract: Planar regions in three-dimensional scenes offer important geometric cues in a variety of three-dimensional perception tasks such as scene understanding, scene reconstruction, and robot navigation. Image analysis to detect planar regions can be performed by a deep learning architecture that includes a number of neural networks configured to estimate parameters for the planar regions. The neural networks process an image to detect an arbitrary number of plane objects in the image. Each plane object is associated with a number of estimated parameters including bounding box parameters, plane normal parameters, and a segmentation mask. Global parameters for the image, including a depth map, can also be estimated by one of the neural networks. Then, a segmentation refinement network jointly optimizes (i.e., refines) the segmentation masks for each instance of the plane objects and combines the refined segmentation masks to generate an aggregate segmentation mask for the image.