3D Point Cloud Processing and Analysis

The field of 3D point cloud processing and analysis is moving towards more efficient and effective methods for object detection, segmentation, and compression. Researchers are exploring new approaches to improve the accuracy and robustness of these methods, such as corner-aware regression for 3D object detection and frequency-disentangled latent triplanes for point cloud compression. Noteworthy papers in this area include:

  • Rethinking the Encoding and Annotating of 3D Bounding Box, which proposes a corner-aligned regression method for 3D object detection.
  • FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds, which introduces a frequency-aware compression model for point clouds.

Sources

Rethinking the Encoding and Annotating of 3D Bounding Box: Corner-Aware 3D Object Detection from Point Clouds

A Storage-Efficient Feature for 3D Concrete Defect Segmentation to Replace Normal Vector

MFM-point: Multi-scale Flow Matching for Point Cloud Generation

FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds

Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation

PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation

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