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FRNet

FRNet is a LiDAR-based 3D semantic segmentation model built on frustum and range-view feature fusion. It is integrated under the segmentation3d task namespace for NuScenes and T4Dataset.

Summary

Property Value
Task 3D semantic segmentation
Modality LiDAR
Input Point cloud
Output Point-wise semantic class labels
Architecture Frustum encoder, FRNet backbone, segmentation heads
Datasets NuScenes, T4Dataset

Available Configurations

Config Name Dataset Purpose
segmentation3d/frnet/hdl32e_nuscenes NuScenes Standard NuScenes configuration
segmentation3d/frnet/ot128_t4dataset_j6gen2 T4Dataset T4Dataset OT128 configuration
segmentation3d/frnet/qt128_t4dataset_j6gen2 T4Dataset T4Dataset QT128 configuration

Training

autoware-ml train --config-name segmentation3d/frnet/hdl32e_nuscenes
autoware-ml train --config-name segmentation3d/frnet/ot128_t4dataset_j6gen2
autoware-ml train --config-name segmentation3d/frnet/qt128_t4dataset_j6gen2

For a pipeline validation run:

autoware-ml train \
    --config-name segmentation3d/frnet/hdl32e_nuscenes \
    +trainer.fast_dev_run=true

Evaluation

autoware-ml test \
    --config-name segmentation3d/frnet/hdl32e_nuscenes \
    +checkpoint=mlruns/segmentation3d/frnet/hdl32e_nuscenes/<run_id>/artifacts/checkpoints/best.ckpt

Deployment

autoware-ml deploy \
    --config-name segmentation3d/frnet/hdl32e_nuscenes \
    +checkpoint=mlruns/segmentation3d/frnet/hdl32e_nuscenes/<run_id>/artifacts/checkpoints/best.ckpt

To validate ONNX export without building a TensorRT engine:

autoware-ml deploy \
    --config-name segmentation3d/frnet/hdl32e_nuscenes \
    +checkpoint=mlruns/segmentation3d/frnet/hdl32e_nuscenes/<run_id>/artifacts/checkpoints/best.ckpt \
    deploy.tensorrt.enabled=false

The exported ONNX model returns point-wise semantic probabilities through a final softmax layer. Training and evaluation continue to use logits inside the Lightning model.

Deployment uses an explicit FRNet export wrapper with copied model submodules and explicit single-sample export metadata, so export-specific behavior does not mutate the training model.

Data Pipeline

The FRNet preprocessing path converts raw point clouds into frustum and range-view representations and prepares both point-level and range-view supervision targets. The training pipeline includes dataset-specific augmentations and FRNet-specific transforms such as frustum mixing, instance copy, and range interpolation.

The standard FRNet training configs follow the AWML experiment contract:

  • AdamW with OneCycleLR
  • step-based validation every 1500 training steps
  • best-checkpoint selection by validation loss
  • mix augmentations that apply a secondary-sample transform pipeline before frustum mixing and instance copy

Implementation

Path Description
autoware_ml/models/segmentation3d/frnet.py FRNet Lightning model wrapper
autoware_ml/models/segmentation3d/encoders/frnet.py Frustum feature encoder
autoware_ml/models/segmentation3d/backbones/frnet.py FRNet backbone
autoware_ml/models/segmentation3d/heads/frnet.py FRNet segmentation heads
autoware_ml/losses/segmentation3d/ Segmentation losses used by FRNet
autoware_ml/datamodule/nuscenes/segmentation3d.py NuScenes segmentation datamodule
autoware_ml/datamodule/t4dataset/segmentation3d.py T4Dataset segmentation datamodule
autoware_ml/transforms/segmentation3d/ Segmentation task transforms used by FRNet
autoware_ml/preprocessing/segmentation3d/frustum_range.py Frustum and range preprocessing
autoware_ml/configs/tasks/segmentation3d/frnet/ Task configurations

Acknowledgment

The Autoware-ML FRNet implementation was ported from the official FRNet project.

  • Repository: https://github.com/Xiangxu-0103/FRNet
  • License: Apache 2.0
  • Paper: Xu, Xiang, et al. "FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation." IEEE Transactions on Image Processing, vol. 34, pp. 2173-2186, 2025.