Mobile laser scanning(MLS)systems mainly comprise laser scanners and mobile mapping platforms.Typical MLS systems can acquire three-dimensional point clouds with 1-10cm point spacings at a normal driving or walking sp...Mobile laser scanning(MLS)systems mainly comprise laser scanners and mobile mapping platforms.Typical MLS systems can acquire three-dimensional point clouds with 1-10cm point spacings at a normal driving or walking speed in streets or indoor environments.The efficiency and stability of these systems make them extremely useful for application in three-dimensional urban modeling.This paper reviews the latest advances of the LiDAR-based mobile mapping system(MMS)point cloud in the field of 3D modeling,including LiDAR simultaneous localization and mapping,point cloud registration,feature extraction,object extraction,semantic segmentation,and processing using deep learning.Furthermore,typical urban modeling applications based on MMS are also discussed.展开更多
The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only addr...The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.展开更多
文摘Mobile laser scanning(MLS)systems mainly comprise laser scanners and mobile mapping platforms.Typical MLS systems can acquire three-dimensional point clouds with 1-10cm point spacings at a normal driving or walking speed in streets or indoor environments.The efficiency and stability of these systems make them extremely useful for application in three-dimensional urban modeling.This paper reviews the latest advances of the LiDAR-based mobile mapping system(MMS)point cloud in the field of 3D modeling,including LiDAR simultaneous localization and mapping,point cloud registration,feature extraction,object extraction,semantic segmentation,and processing using deep learning.Furthermore,typical urban modeling applications based on MMS are also discussed.
基金supported by the National Natural Science Foundation of China(Grant No.62171393)National Key R&D Program of China(Grant No.2021YFF0704600).
文摘The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.
