VIEW SYNTHESIS FOR SELF-DRIVING
    1.
    发明申请

    公开(公告)号:US20250118009A1

    公开(公告)日:2025-04-10

    申请号:US18903348

    申请日:2024-10-01

    Abstract: A computer-implemented method for synthesizing an image includes capturing data from a scene and fusing grid-based representations of the scene from different encodings to inherit beneficial properties of the different encodings, The encodings include Lidar encoding and a high definition map encoding. Rays are rendered from fused grid-based representations. A density and color are determined for points in the rays. A volume rendering is employed for the rays with the density and color. An image is synthesized from the volume rendered rays with the density and the color.

    AUTOMATIC MULTI-MODALITY SENSOR CALIBRATION WITH NEAR-INFRARED IMAGES

    公开(公告)号:US20250117029A1

    公开(公告)日:2025-04-10

    申请号:US18905280

    申请日:2024-10-03

    Abstract: Systems and methods for automatic multi-modality sensor calibration with near-infrared images (NIR). Image keypoints from collected images and NIR keypoints from NIR can be detected. A deep-learning-based neural network that learns relation graphs between the image keypoints and the NIR keypoints can match the image keypoints and the NIR keypoints. Three dimensional (3D) points from 3D point cloud data can be filtered based on corresponding 3D points from the NIR keypoints (NIR-to-3D points) to obtain filtered NIR-to-3D points. An extrinsic calibration can be optimized based on a reprojection error computed from the filtered NIR-to-3D points to obtain an optimized extrinsic calibration for an autonomous entity control system. An entity can be controlled by employing the optimized extrinsic calibration for the autonomous entity control system.

    CAMERA SELF-CALIBRATION NETWORK
    5.
    发明申请

    公开(公告)号:US20200234467A1

    公开(公告)日:2020-07-23

    申请号:US16736451

    申请日:2020-01-07

    Abstract: Systems and methods for camera self-calibration are provided. The method includes receiving real uncalibrated images, and estimating, using a camera self-calibration network, multiple predicted camera parameters corresponding to the real uncalibrated images. Deep supervision is implemented based on a dependence order between the plurality of predicted camera parameters to place supervision signals across multiple layers according to the dependence order. The method also includes determining calibrated images using the real uncalibrated images and the predicted camera parameters.

    Multi-modal test-time adaptation
    6.
    发明授权

    公开(公告)号:US12254681B2

    公开(公告)日:2025-03-18

    申请号:US17903393

    申请日:2022-09-06

    Abstract: Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels. The method further includes selecting confident pseudo-labels from the robust pseudo labels and measured prediction consistencies to form a final cross-modal pseudo-label set as a self-training signal, and updating batch parameters utilizing the self-training signal.

    IMAGE FEATURE MATCHING WITH FORMAL PRIVACY GUARANTEES

    公开(公告)号:US20240303365A1

    公开(公告)日:2024-09-12

    申请号:US18598198

    申请日:2024-03-07

    CPC classification number: G06F21/6227 G06V10/751

    Abstract: Systems and methods are provided for privacy-preserving image feature matching in computer vision applications, including receiving a raw image descriptor, and perturbing the raw image descriptor using a subset selection mechanism to generate a perturbed descriptor set that includes the raw image descriptor and additional descriptors. Each descriptor in the perturbed descriptor set is replaced with its nearest neighbor in a predefined descriptor database to reduce the output domain size of the subset selection mechanism. Local differential privacy (LDP) protocols are employed to further perturb the descriptor set, ensuring formal privacy guarantees, and the perturbed descriptor set is matched against a second set of descriptors for image feature matching.

    Joint rolling shutter image stitching and rectification

    公开(公告)号:US11694311B2

    公开(公告)日:2023-07-04

    申请号:US17182836

    申请日:2021-02-23

    CPC classification number: G06T5/003 G06T7/20 H04N23/689

    Abstract: A computer-implemented method executed by at least one processor for applying rolling shutter (RS)-aware spatially varying differential homography fields for simultaneous RS distortion removal and image stitching is presented. The method includes inputting two consecutive frames including RS distortions from a video stream, performing keypoint detection and matching to extract correspondences between the two consecutive frames, feeding the correspondences between the two consecutive frames into an RS-aware differential homography estimation component to filter out outlier correspondences, sending inlier correspondences to an RS-aware spatially varying differential homography field estimation component to compute an RS-aware spatially varying differential homography field, and using the RS-aware spatially varying differential homography field in an RS stitching and correction component to produce stitched images with removal of the RS distortions.

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