A 3D anomaly detection method that uses internal z-axis projection slices and Laplacian feature filtering reports state-of-the-art results on Real3D-AD and Anomaly-ShapeNet.
Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform
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abstract
3D sensing and content capture have made significant progress in recent years and the MPEG standardization organization is launching a new project on immersive media with point cloud compression (PCC) as one key corner stone. In this work, we introduce a new binary tree based point cloud content partition and explore the graph signal processing tools, especially the graph transform with optimized Laplacian sparsity, to achieve better energy compaction and compression efficiency. The resulting rate-distortion operating points are convex-hull optimized over the existing Lagrangian solutions. Simulation results with the latest high quality point cloud content captured from the MPEG PCC demonstrated the transform efficiency and rate-distortion (R-D) optimal potential of the proposed solutions.
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2024 1verdicts
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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
A 3D anomaly detection method that uses internal z-axis projection slices and Laplacian feature filtering reports state-of-the-art results on Real3D-AD and Anomaly-ShapeNet.