公开(公告)号：US20220092855A1

公开(公告)日：2022-03-24

申请号：US17457825

申请日：2021-12-06

Applicant: NVIDIA Corporation

Inventor： Dongwoo Lee ,  Junghyun Kwon ,  Sangmin Oh ,  Wenchao Zheng ,  Hae-Jong Seo ,  David Nister ,  Berta Rodriguez Hervas

IPC: G06T17/30 ,  G06T7/40

Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

公开(公告)号：US20220019893A1

公开(公告)日：2022-01-20

申请号：US17449310

申请日：2021-09-29

Applicant: NVIDIA Corporation

Inventor： Junghyun Kwon ,  Yilin Yang ,  Bala Siva Sashank Jujjavarapu ,  Zhaoting Ye ,  Sangmin Oh ,  Minwoo Park ,  David Nister

IPC: G06N3/08 ,  B60W30/14 ,  B60W60/00 ,  G06K9/62 ,  G06K9/00

Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.

公开(公告)号：US20210264175A1

公开(公告)日：2021-08-26

申请号：US17187228

申请日：2021-02-26

Applicant: NVIDIA Corporation

Inventor： Dong Zhang ,  Sangmin Oh ,  Junghyun Kwon ,  Baris Evrim Demiroz ,  Tae Eun Choe ,  Minwoo Park ,  Chethan Ningaraju ,  Hao Tsui ,  Eric Viscito ,  Jagadeesh Sankaran ,  Yongqing Liang

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/32 ,  G06N3/08 ,  B60W60/00

Abstract: Systems and methods are disclosed that use a geometric approach to detect objects on a road surface. A set of points within a region of interest between a first frame and a second frame are captured and tracked to determine a difference in location between the set of points in two frames. The first frame may be aligned with the second frame and the first pixel values of the first frame may be compared with the second pixel values of the second frame to generate a disparity image including third pixels. One or more subsets of the third pixels that have an disparity image value about a first threshold may be combined, and the third pixels may be scored and associated with disparity values for each pixel of the one or more subsets of the third pixels. A bounding shape may be generated based on the scoring that corresponds to the object.

公开(公告)号：US20200294310A1

公开(公告)日：2020-09-17

申请号：US16820164

申请日：2020-03-16

Applicant: NVIDIA CORPORATION

Inventor： Dongwoo Lee ,  Junghyun Kwon ,  Sangmin Oh ,  Wenchao Zheng ,  Hae-Jong Seo ,  David Nister ,  Berta Rodriguez Hervas

IPC: G06T17/30 ,  G06T7/40

Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.