Table Of Contents
The decodeBbox3DPlugin performs 3D bounding boxes decoding for PointPillars model.
decodeBbox3DPlugin implements the 3D bounding boxes decoding. It applies deltas to anchor boxes and produces the 3D bounding boxes in the format: (x, y, z, dx, dy, dz, rotation). The deltas are the input tensors of this plugin, and the anchors are computed on-the-fly based on the parameters passed in from the corresponding node in ONNX graph.
This plugin is optimized for the above steps and it allows you to do PointPillars inference in TensorRT.
The decodeBbox3DPlugin takes 3 inputs; cls_preds, box_preds, and dir_cls_preds.
cls_preds
Predicted confidence(score) values. This tensor has a shape [N, H, W, A*C], where N is the batch size, H is the height of the feature map, W is the width of the feature map, and A is the number of anchor boxes per grid point on the feature map, and C is the number of object classes.
box_preds
Predicted per-class bounding box delta values. This tensor has a shape [N, H, W, A*7], where N, H, W, A are as above, and 7 is just the size of a bounding box delta. The decoded bounding box also have a length of 7 and are represented as (x, y, z, dx, dy, dz, rotation).
dir_cls_preds
Predicted direction confidence(score) values. There are two directions for each bounding box(0 and 1). The one with the larger score will be the final detected direction and the direction value(0 or 1) will be used to compute the rotation values in 3D bounding boxes.
The decodeBbox3DPlugin generates the following 2 outputs:
output_boxes
The decoded 3D bounding boxes. This tensor has a shape of [N, H*W*A, 9], where N, H, W, A are as above, and the number 9 represents the bounding boxes(7) plus the score(1) and class ID(1). The last dimension are encoded as (x, y, z, dx, dy, dz, rotation, class_id, score).
num_boxes
The number of valid bounding boxes in output_boxes. Bounding boxes whose score are higher than a certain threshold is regarded as a valid bounding box. The score threshold is a parameter of this plugin(see below). With the number of valid boxes, we can easily retrieve the useful bounding boxes from thousands of output_boxes.
The decoding will produce num_boxes number of boxes and those boxes can be applied to a 3D Non-Maximum-Suppression(NMS) operation(not covered in this plugin) to eliminate highly overlapped boxes and produce the final detection result.
decodeBbox3DPlugin has plugin creator class decodeBbox3DPluginCreator and plugin class decodeBbox3DPlugin.
The parameters are defined below and consists of the following attributes:
| Type | Parameter | Description |
|---|---|---|
list of floats |
anchors |
The anchor sizes and aspect ratios. |
list of floats |
anchor_bottom_height |
The heights of the anchor bottom, per class. |
float |
dir_limit_offset |
Direction limit offset. |
float |
dir_offset |
Direction offset. |
int |
num_dir_bins |
Number of direction bins. |
list of floats |
point_cloud_range |
The range of point cloud coordinates. |
float |
score_thresh |
The score threshold for bounding boxes. |
The following resources provide a deeper understanding of the decodeBbox3DPlugin plugin:
Networks:
Documentation:
For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.
Dec 2021
This is the first release of this README.md file.
There are no known issues in this plugin.