Volumetric grid is widely used for 3D deep learning due to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally inefficient. In this work, we propose the PointGrid, a 3D convolutional network that incorporates a constant number of points within each grid cell thus allowing the network to learn higher order local approximation functions that could better represent the local geometry shape details. With experiments on popular shape recognition benchmarks, PointGrid demonstrates state-of-the-art performance over existing deep learning methods on both classification and segmentation.

Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern sensing technology, these measurements can be taken on 3-D point clouds and 2-D optical images that provide a high level of structural accuracy and visual detail. Doing so allows engineers to obtain the needed data with relative ease while eliminating the biases and hazards inherent in taking manual measurements. This letter presents an approach for fusing the 2-D and 3-D data in natural and unstructured scenes. This includes a novel method for visualizing imagery obtained with very different sensors to maximize their visual similarity making registration a more tangible task. To show the effectiveness of our registration methodology, we evaluate measurements taken manually and digitally on rock facet and cut discontinuity orientations in Rolla, MO. Our method is able to align the 2-D and 3-D data with an accuracy of under 2 cm. The median difference between measurements manually obtained by a geological engineer and those obtained with our proposed software is 3.65.

Circle detection from digital images is a necessary operation in many robotics and computer vision tasks to facilitate shape and object recognition. We propose and analyze a novel method, based on line segment detection and circle completeness verification, to detect circles in images. The key idea is to use line segments instead of raw edge pixels to get the circle candidates followed by a verification step to measure the circle's completeness. Experimental results on several synthesized and hand-sketched as well as natural images with various complication favor the accuracy, robustness and efficiency of our approach against other well-known techniques. Our method can deal with incomplete, cocentric, discontinuous and occluded circles with noise and deformation. Moreover, in this paper, we create CDBD, the first benchmark dataset for circle detection with ground truth circles labeled by human, which will establish standard quantitative results in future research regarding circle detection.

LIDAR range scans can be used to quickly create accurate 3D models for virtual reality and as a basis to visualize sets of photographs, videos, and virtual objects in a cohesive environment. The number of existing virtual reality programs that use LIDAR data as input has motivated our group to develop methods for fusing images with 3D scans and for augmenting the scans with both dynamic objects present in videos and virtual models. Bringing together as many data sources as possible increases users' abilities to present related information in one, intuitive venue. We demonstrate how to register a variety of 2D imagery with a range scan to construct photo-realistic models and to extract walking people captured in videos and model them in a 3D space. We also present a method for determining the sun position from a set of stitched photographs in order to apply correct lighting to virtual objects placed amongst real world data. Naturally lit objects can be inserted into original photographs using our 2D-3D registration information. These methods are all combined to display and study photos, videos, and virtual objects in a complete 3D environment.