pointpillars

notebooks/3D-point-pillars/README.md

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+# **PointPillar for 3D object detection**
+
+PointPillar is a fast and efficient deep-learning architecture for 3D object detection from LiDAR point clouds, commonly used in autonomous driving.
+
+Instead of operating directly on raw points or dense 3D voxels, PointPillar groups points into vertical columns ("pillars") and encodes per-pillar features. These pillar features are arranged into a pseudo-image that a 2D convolutional backbone can process. The pipeline is lightweight and well-suited for real-time inference.
+
+Core stages:
+- Voxelization / Pillarization: group points into pillars and compute per-pillar statistics.
+- Pillar feature encoding: a small network encodes points in each pillar into a fixed-size feature vector.
+- Scatter to pseudo-image: place each pillar's feature into a 2D grid (pseudo-image) based on the pillar's XY location.
+- 2D backbone + neck: apply 2D convolutions to produce multi-scale feature maps.
+- Detection head: predict class scores, bounding box regressions, and directions on the pseudo-image.
+- Post-processing: decode boxes, apply non-maximum suppression (NMS), and output final detections.
+
+In this tutorial we consider how to run PointPillars with OpenVINO.
+
+## Notebook contents
+The tutorial consists from following steps:
+
+- Install requirements
+- Build extensions
+- Exporting the model for OpenVINO
+- Run OpenVINO model inference
+
+## Installation instructions
+This is a self-contained example that relies solely on its own code.</br>
+We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/fireredtts2/README.md" />