LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical sensors 10 to 20 Hz, much lower than other commonly used like cameras. To overcome temporal limitations sensors, a novel task named Point Cloud Frame Interpolation studied this paper. Given two consecutive frames, aims generate intermediate frame(s) ...

Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of published tools do not implement methods to consider complex objects, such as greenery their models or they expect a rather mesh represent objects. Their use an environment that is difficult geometrically, vegetation-covered areas, limited. In this paper, we present newly developed tool focused...

Point cloud panoptic segmentation is a challenging task that seeks holistic solution for both semantic and instance to predict groupings of coherent points. Previous approaches treat as surrogate tasks, they either use clustering methods or bounding boxes gather with costly computation hand-craft designs in the task. In this paper, we propose simple but effective point unified (PUPS) framework,...

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines. However, cloud data is inherently sparse and irregular, causing significant difficulties for machine perception. In this work, we focus on upsampling task that intends to generate dense high-fidelity from input data. Specifically, activate transformer’s strong capability representing features, d...

Following the tremendous success of transformer in natural language processing and image understanding tasks, this paper, we present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet), which mainly consists Point Cloud (DPCT) module. Specifically, by aggregating well-designed point-wise channel-wise multi-head self-attention models simultaneously, D...

This paper describes a methodology for obtaining a high resolution dense point cloud using Kinect (J. Smisek and Pajdla, 2011) and HD cameras. Kinect produces a VGA resolution photograph and a noisy point cloud. But high resolution images of the same scene can easily be obtained using additional HD cameras. We combine the information to generate a high resolution dense point cloud. First, we do...