Towards Optimal Point Cloud Processing for 3D Reconstruction

This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods.

The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods  for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data.

The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.

Dr. Guoxiang Zhang completed his Ph.D. program at University of California Merced in Spring 2021. His research interests are in 3D point cloud processing, semantic mapping with applications in cave exploration drones and autonomous driving systems. The loop-detection method he invented has the top performance on several real world datasets. His work on more informed RANSAC won the best presentation award from the 2020 ICCMA conference. His foundational code contributions in Julia for 3D reconstruction application are being widely used.