Advances in Point Clouds for Sensing Applications

Dear Colleagues,

Point clouds have become a universal representation for 3D sensing across LiDAR, radar, RGB-D cameras, event sensors, and multi-view photogrammetry. Rapid progress in sensor design, geometric learning, and efficient computing is reshaping how we acquire, fuse, and interpret sparse, noisy, and large-scale 3D data. However, fundamental challenges remain in handling occlusions and dynamic scenes; aligning heterogeneous viewpoints; quantifying uncertainty; compressing and streaming data for edge devices; and ensuring interoperability, reproducibility, and real-time performance in safety-critical settings.

This Special Issue, Advances in Point Clouds for Sensing Applications, welcomes contributions that push the state of the art in 3D perception and mapping for domains such as autonomous driving, robotics, AR/VR, digital twins, smart cities, infrastructure inspection, and geosciences. We invite novel methods for point-cloud acquisition and reconstruction; registration, SLAM, and tracking; denoising, completion, and super-resolution; semantic/instance segmentation and object detection; multimodal sensor fusion (e.g., LiDAR, camera, radar); compression, indexing, and retrieval; uncertainty modeling and calibration; and energy-/latency-aware processing on edge platforms. Benchmark datasets, standardized evaluation protocols, and reports from real-world deployments are particularly encouraged. Our goal is to catalyze robust, scalable, and trustworthy 3D sensing pipelines that translate advances in algorithms and hardware into measurable impacts.

Dr. Luis G. Jaimes
Guest Editor

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