Beta

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

Point Cloud Segmentation¶

The Task¶

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

PointCloud Segmentation is the task of performing classification at a point-level, meaning each point will associated to a given class. 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 multiple folder, one for each sequence and a meta.yaml file describing the classes and their official associated color map. A sequence should contain one folder for scans and one folder for labels, plus a pose.txt to re-align the sequence if required. Here’s the structure:

data
├── meta.yaml
├── 00
│   ├── scans
|   |    ├── 00000.bin
|   |    ├── 00001.bin
|   |    ...
│   ├── labels
|   |    ├── 00000.label
|   |    ├── 00001.label
|   |   ...
|   ├── pose.txt
│   ...
|
└── XX
   ├── scans
   |    ├── 00000.bin
   |    ├── 00001.bin
   |    ...
   ├── labels
   |    ├── 00000.label
   |    ├── 00001.label
   |   ...
   ├── pose.txt

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

Once we’ve downloaded the data using download_data(), we create the PointCloudSegmentationData. We select a pre-trained randlanet_semantic_kitti backbone for our PointCloudSegmentation task. We then use the trained PointCloudSegmentation 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 PointCloudSegmentation, PointCloudSegmentationData

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

datamodule = PointCloudSegmentationData.from_folders(
    train_folder="data/SemanticKittiTiny/train",
    val_folder="data/SemanticKittiTiny/val",
    batch_size=4,
)

# 2. Build the task
model = PointCloudSegmentation(backbone="randlanet_semantic_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 = PointCloudSegmentationData.from_files(
    predict_files=[
        "data/SemanticKittiTiny/predict/000000.bin",
        "data/SemanticKittiTiny/predict/000001.bin",
    ],
    batch_size=4,
)
predictions = trainer.predict(model, datamodule=datamodule)
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("pointcloud_segmentation_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/getting_started_ml_visualizer.gif

Flash Zero¶

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

flash pointcloud_segmentation

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

flash pointcloud_segmentation --help