Abstract: Systems and methods for object detection. The object detection may be used to control an autonomous vehicle. For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: matching points of the LiDAR dataset to pixels in the at least one image; and detecting the object in a point cloud defined by the LiDAR dataset based on the matching. The object detection may be used to facilitate at least one autonomous driving operation (e.g., autonomous driving operation comprises an object tracking operation, an object trajectory prediction operation, a vehicle trajectory determination operation, and/or a collision avoidance operation).

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object is detected by performing the following operations: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: Systems and methods for object detection. The object detection may be used to control an autonomous vehicle. For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: matching points of the LiDAR dataset to pixels in the at least one image; and detecting the object in a point cloud defined by the LiDAR dataset based on the matching. The object detection may be used to facilitate at least one autonomous driving operation (e.g., autonomous driving operation comprises an object tracking operation, an object trajectory prediction operation, a vehicle trajectory determination operation, and/or a collision avoidance operation).

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object is detected by performing the following operations: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device. The LiDAR dataset and image(s) are used to detect an object that is in proximity to the autonomous vehicle. The object is detected by: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; and detecting the object in a point cloud defined by the LiDAR dataset based on the plurality of segments of LiDAR data points. The object detection may be used to facilitate at least one autonomous driving operation.

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object being is detected by: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: Systems/methods for object detection. The methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by a computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: generating a pruned LiDAR dataset by reducing a total number of points contained in the LiDAR dataset; and detecting the object in a point cloud defined by the pruned LiDAR dataset. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: In one example implementation, a system for aligning of images of a three-dimensional (3D) object includes a select engine to select a first image and a second image from a sequence of images of a 3D object taken at different object orientations from a capture cycle of a scanner, a combine engine to combine first metadata from the first image and second metadata from the second image to form combined metadata, an orientation engine to select a third image from the sequence of images, where the third image is from an opposite object orientation relative to a first object orientation of the first image or a second object orientation of the second image, and an align engine to align the sequence of images based on the combined metadata and third metadata from the third image.

Abstract: In one example implementation, a system for aligning of images of a three-dimensional (3D) object includes a select engine to select a first image and a second image from a sequence of images of a 3D object taken at different object orientations from a capture cycle of a scanner, a combine engine to combine first metadata from the first image and second metadata from the second image to form combined metadata, an orientation engine to select a third image from the sequence of images, where the third image is from an opposite object orientation relative to a first object orientation of the first image or a second object orientation of the second image, and an align engine to align the sequence of images based on the combined metadata and third metadata from the third image.