Beta

Point cloud object detection is currently in Beta. The API and functionality may change without warning in future releases. More details.

Point Cloud Object Detection¶

The Task¶

A Point Cloud is a set of data points in space, usually describes by x, y and z coordinates.

PointCloud Object Detection is the task of identifying 3D objects in point clouds and their associated classes and 3D bounding boxes.

The current integration builds on top Open3D-ML.

Example¶

Let’s look at an example using a data set generated from the KITTI Vision Benchmark. The data are a tiny subset of the original dataset and contains sequences of point clouds.

The data contains:

one folder for scans
one folder for scan calibrations
one folder for labels
a meta.yaml file describing the classes and their official associated color map.

Here’s the structure:

data
├── meta.yaml
├── train
│   ├── scans
|   |    ├── 00000.bin
|   |    ├── 00001.bin
|   |    ...
│   ├── calibs
|   |    ├── 00000.txt
|   |    ├── 00001.txt
|   |   ...
│   ├── labels
|   |    ├── 00000.txt
|   |    ├── 00001.txt
│   ...
├── val
│   ...
├── predict
    ├── scans
    |   ├── 00000.bin
    |   ├── 00001.bin
    |
    ├── calibs
    |   ├── 00000.txt
    |   ├── 00001.txt
    ├── meta.yaml

Learn more: http://www.semantic-kitti.org/dataset.html

Once we’ve downloaded the data using download_data(), we create the PointCloudObjectDetectorData. We select a pre-trained randlanet_semantic_kitti backbone for our PointCloudObjectDetector task. We then use the trained PointCloudObjectDetector for inference. Finally, we save the model. Here’s the full example:

import flash
import torch
from flash.core.data.utils import download_data
from flash.pointcloud import PointCloudObjectDetector, PointCloudObjectDetectorData

# 1. Create the DataModule
# Dataset Credit: http://www.semantic-kitti.org/
download_data("https://pl-flash-data.s3.amazonaws.com/KITTI_tiny.zip", "data/")

datamodule = PointCloudObjectDetectorData.from_folders(
    train_folder="data/KITTI_Tiny/Kitti/train",
    val_folder="data/KITTI_Tiny/Kitti/val",
    batch_size=4,
)

# 2. Build the task
model = PointCloudObjectDetector(backbone="pointpillars_kitti", num_classes=datamodule.num_classes)

# 3. Create the trainer and finetune the model
trainer = flash.Trainer(
    max_epochs=1, limit_train_batches=1, limit_val_batches=1, num_sanity_val_steps=0, gpus=torch.cuda.device_count()
)
trainer.fit(model, datamodule)

# 4. Predict what's within a few PointClouds?
datamodule = PointCloudObjectDetectorData.from_files(
    predict_files=[
        "data/KITTI_Tiny/Kitti/predict/scans/000000.bin",
        "data/KITTI_Tiny/Kitti/predict/scans/000001.bin",
    ],
    batch_size=4,
)
predictions = trainer.predict(model, datamodule=datamodule)
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("pointcloud_detection_model.pt")

To learn how to view the available backbones / heads for this task, see Backbones and Heads.

https://raw.githubusercontent.com/intel-isl/Open3D-ML/master/docs/images/visualizer_BoundingBoxes.png

Flash Zero¶

The point cloud object detector can be used directly from the command line with zero code using Flash Zero. You can run the above example with:

flash pointcloud_detection

To view configuration options and options for running the point cloud object detector with your own data, use:

flash pointcloud_detection --help