v0.49.1

See https://github.com/qualcomm/ai-hub-models/releases/v0.49.1 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/nikitakaraevv/pointnet/blob/master/LICENSE.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- bu_auto
+- android
+pipeline_tag: image-segmentation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pointnet/web-assets/model_demo.png)
+
+# 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](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/pointnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+
+Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) 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 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pointnet/releases/v0.49.1/pointnet-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pointnet/releases/v0.49.1/pointnet-qnn_dlc-float.zip)
+| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pointnet/releases/v0.49.1/pointnet-tflite-float.zip)
+
+For more device-specific assets and performance metrics, visit **[PointNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pointnet)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/pointnet) 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](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/pointnet) 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® 8 Elite Gen 5 Mobile | 0.321 ms | 0 - 29 MB | NPU
+| PointNet | ONNX | float | Snapdragon® X2 Elite | 0.306 ms | 7 - 7 MB | NPU
+| PointNet | ONNX | float | Snapdragon® X Elite | 0.792 ms | 7 - 7 MB | NPU
+| PointNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.393 ms | 0 - 47 MB | NPU
+| PointNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.649 ms | 0 - 8 MB | NPU
+| PointNet | ONNX | float | Qualcomm® QCS9075 | 0.811 ms | 0 - 3 MB | NPU
+| PointNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.437 ms | 0 - 30 MB | NPU
+| PointNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.322 ms | 0 - 30 MB | NPU
+| PointNet | QNN_DLC | float | Snapdragon® X2 Elite | 0.43 ms | 0 - 0 MB | NPU
+| PointNet | QNN_DLC | float | Snapdragon® X Elite | 0.785 ms | 0 - 0 MB | NPU
+| PointNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.416 ms | 0 - 43 MB | NPU
+| PointNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1.85 ms | 0 - 26 MB | NPU
+| PointNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.66 ms | 0 - 7 MB | NPU
+| PointNet | QNN_DLC | float | Qualcomm® QCS9075 | 0.801 ms | 0 - 2 MB | NPU
+| PointNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 0.867 ms | 0 - 43 MB | NPU
+| PointNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.485 ms | 0 - 30 MB | NPU
+| PointNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.319 ms | 0 - 30 MB | NPU
+| PointNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.412 ms | 0 - 43 MB | NPU
+| PointNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 1.889 ms | 0 - 27 MB | NPU
+| PointNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.661 ms | 0 - 1 MB | NPU
+| PointNet | TFLITE | float | Qualcomm® QCS9075 | 0.811 ms | 0 - 9 MB | NPU
+| PointNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 0.869 ms | 0 - 44 MB | NPU
+| PointNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.484 ms | 0 - 27 MB | NPU
+
+## License
+* The license for the original implementation of PointNet can be found
+  [here](https://github.com/nikitakaraevv/pointnet/blob/master/LICENSE).
+
+
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).