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Image Segmentation
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bu_auto
android
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metadata 
library_name: pytorch
license: other
tags:
  - bu_auto
  - android
pipeline_tag: image-segmentation

PointNet: Optimized for Qualcomm Devices

PointNet is a pioneering neural network architecture designed to directly consume unordered point cloud data for tasks such as classification and segmentation. It learns spatial features from raw 3D points without requiring voxelization or image projections.

This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

Runtime Precision Chipset SDK Versions Download
ONNX float Universal QAIRT 2.45, ONNX Runtime 1.25.0 Download
QNN_DLC float Universal QAIRT 2.45 Download
TFLITE float Universal QAIRT 2.45 Download

For more device-specific assets and performance metrics, visit PointNet on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

  • Custom weights (e.g., fine-tuned checkpoints)
  • Custom input shapes
  • Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for PointNet on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.semantic_segmentation

Model Stats:

  • Model checkpoint: save
  • Input resolution: 1x3x1024
  • Model size: 13.2 MB

Performance Summary

Model Runtime Precision Chipset Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit
PointNet ONNX float Snapdragon® X2 Elite 0.305 ms 213 - 213 MB NPU
PointNet ONNX float Snapdragon® X Elite 0.655 ms 149 - 149 MB NPU
PointNet ONNX float Snapdragon® 8 Gen 3 Mobile 0.406 ms 59 - 91 MB NPU
PointNet ONNX float Snapdragon® 8 Gen 1 Mobile 0.865 ms 0 - 33 MB NPU
PointNet ONNX float Qualcomm® QCS8550 (Proxy) 0.647 ms 0 - 12 MB NPU
PointNet ONNX float Qualcomm® QCS8450 0.865 ms 0 - 33 MB NPU
PointNet ONNX float Snapdragon® 8 Elite Mobile 0.421 ms 0 - 24 MB NPU
PointNet ONNX float Snapdragon® 8 Elite Gen 5 Mobile 0.322 ms 0 - 19 MB NPU
PointNet ONNX float Qualcomm® QCS9075 0.807 ms 0 - 52 MB NPU
PointNet ONNX float Qualcomm® QCS8750 0.421 ms 0 - 24 MB NPU
PointNet ONNX float Qualcomm® QCS7181 0.655 ms 149 - 149 MB NPU
PointNet QNN_DLC float Snapdragon® X2 Elite 0.403 ms 0 - 0 MB NPU
PointNet QNN_DLC float Snapdragon® X Elite 0.771 ms 0 - 0 MB NPU
PointNet QNN_DLC float Snapdragon® 8 Gen 3 Mobile 0.41 ms 0 - 42 MB NPU
PointNet QNN_DLC float Snapdragon® 8 Gen 1 Mobile 0.865 ms 0 - 47 MB NPU
PointNet QNN_DLC float Qualcomm® QCS8275 1.848 ms 0 - 23 MB NPU
PointNet QNN_DLC float Qualcomm® QCS8550 (Proxy) 0.659 ms 0 - 67 MB NPU
PointNet QNN_DLC float Qualcomm® QCS8450 0.865 ms 0 - 47 MB NPU
PointNet QNN_DLC float Snapdragon® 8 Elite Mobile 0.485 ms 0 - 23 MB NPU
PointNet QNN_DLC float Qualcomm® SA8295P 1.119 ms 0 - 20 MB NPU
PointNet QNN_DLC float Snapdragon® 8 Elite Gen 5 Mobile 0.323 ms 0 - 23 MB NPU
PointNet QNN_DLC float Qualcomm® SA7255P 1.848 ms 0 - 23 MB NPU
PointNet QNN_DLC float Qualcomm® QCS9075 0.799 ms 0 - 2 MB NPU
PointNet QNN_DLC float Qualcomm® QCS8750 0.485 ms 0 - 23 MB NPU
PointNet QNN_DLC float Qualcomm® QCS7181 0.771 ms 0 - 0 MB NPU
PointNet TFLITE float Snapdragon® 8 Gen 3 Mobile 0.41 ms 0 - 40 MB NPU
PointNet TFLITE float Snapdragon® 8 Gen 1 Mobile 0.861 ms 0 - 46 MB NPU
PointNet TFLITE float Qualcomm® QCS8275 1.885 ms 0 - 24 MB NPU
PointNet TFLITE float Qualcomm® QCS8550 (Proxy) 0.655 ms 0 - 2 MB NPU
PointNet TFLITE float Qualcomm® SA8775P 4.276 ms 0 - 16 MB GPU
PointNet TFLITE float Qualcomm® SA8650P 4.276 ms 0 - 16 MB GPU
PointNet TFLITE float Qualcomm® SA8255P 4.276 ms 0 - 16 MB GPU
PointNet TFLITE float Qualcomm® QCS8450 0.861 ms 0 - 46 MB NPU
PointNet TFLITE float Snapdragon® 8 Elite Mobile 0.483 ms 0 - 24 MB NPU
PointNet TFLITE float Qualcomm® SA8295P 1.122 ms 0 - 21 MB NPU
PointNet TFLITE float Snapdragon® 8 Elite Gen 5 Mobile 0.324 ms 0 - 24 MB NPU
PointNet TFLITE float Qualcomm® SA7255P 1.885 ms 0 - 24 MB NPU
PointNet TFLITE float Qualcomm® QCS9075 0.801 ms 0 - 8 MB NPU
PointNet TFLITE float Qualcomm® QCS8750 0.483 ms 0 - 24 MB NPU

License

  • The license for the original implementation of PointNet can be found here.

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