Method, apparatus and computer program product for three dimensional feature extraction from a point cloud
AssigneeHERE Global B.V.
InventorShubhabrata Roy
Life of this patent
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Abstract
Provided herein is a method, apparatus, and computer program product for classifying objects as static objects or dynamic objects based on point cloud data. Methods may include: receiving point cloud data representative of an environment; computing voxel sequences from the point cloud data; extracting voxel-wise semantic features from the voxel sequences; modeling voxel-wise temporal changes based on the voxel-wise semantic features; and classifying objects in the environment as dynamic objects or static objects based on the modeled voxel-wise temporal changes. Computing voxel sequences from the point cloud data may include using a voxel cloud connectivity segmentation method to group voxels in point clouds into perceptually meaningful regions.
Claims14
An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to at least:
- receive point cloud data representative of an environment captured by a LIDAR sensor;
- compute voxel sequences from the point cloud data;
- extract voxel-wise semantic features from the voxel sequences;
- model voxel-wise temporal changes for each of a plurality of locations based on the voxel-wise semantic features using a convolutional long short-term memory network to generate the model with each of the plurality of locations weighted based on convolutional neural network features;
- and
- classify objects in the environment as dynamic objects or static objects based on the modeled voxel-wise temporal changes and based on output of a spatial transformer network provided to a three-dimensional conditional random field layer to distinguish between LIDAR sensor movement and object movement to identify objects in the environment as dynamic objects or static objects.
A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions to:
- receive point cloud data representative of an environment captured by a LIDAR sensor;
- compute voxel sequences from the point cloud data;
- extract voxel-wise semantic features from the voxel sequences;
- model voxel-wise temporal changes for each of a plurality of locations based on the voxel-wise semantic features using a convolutional long short-term memory network to generate the model with each of the plurality of locations weighted based on convolutional neural network features;
- and
- classify objects in the environment as dynamic objects or static objects based on the modeled voxel-wise temporal changes and based on output of a spatial transformer network provided to a three-dimensional conditional random field layer to distinguish between LIDAR sensor movement and object movement to identify objects in the environment as dynamic objects or static objects.
A method comprising:
- receiving point cloud data representative of an environment captured by a LIDAR sensor;
- computing voxel sequences from the point cloud data;
- extracting voxel-wise semantic features from the voxel sequences;
- modeling voxel-wise temporal changes for each of a plurality of locations based on the voxel-wise semantic features using a convolutional long short-term memory network to generate the model with each of the plurality of locations weighted based on convolutional neural network features;
- and
- classifying objects in the environment as dynamic objects or static objects based on the modeled voxel-wise temporal changes and based on output of a spatial transformer network provided to a three-dimensional conditional random field layer to distinguish between LIDAR sensor movement and object movement to identify objects in the environment as dynamic objects or static objects.
Dependencies are inferred from the claim text.