Det2Seg: A TwoStage Approach for Road Object Segmentation from 3D Point Clouds Object segmentation from 3D point clouds is an important topic in realworld applications. However, due to the sparsity and irregularity of point clouds, instance segmentation often suffers unsatisfying performance in various scenes such as autonomous driving. There are two main difficulties. First, in a wild scene, background noise often occupies a majority of the entire point set. Second, smallscale objects are often difficult to be recognized due to larger uncertainty. In this paper, we propose Det2Seg, a twostage approach which alleviates the above issues towards more accurate instance segmentation of roadobjects. In the first stage, we adopt Pointpillars 1 to detect the regionsof interest that can localize and classify objects in a coarse level; in the second stage, we extract points from the detected regions into pillars, encode them into a new data format and feed it into a 2D convolutional neural network to perform finegrained, domainspecific instance segmentation. We evaluate our approach on raw LiDAR (Light Detection And Ranging) data from the KITTI dataset 2. The experimental results show that our approach largely outperforms the prior researches. In particular, our approach stands out for its significant ability on recognizing and segmenting smallscale objects, i.e., an improvement of over 20% in terms of Intersection over Union (IoU), beyond stateof thearts, is obtained for the cyclist class.

Unsupervised Segmentation Of Urban 3D Point Cloud Based On Lidarimage As one of the most vital fields in 3D scene understanding, segmentation of lidar point cloud has verity of applications such as unmanned vehicle. In order to deal with the problem of limited local computing resources, this paper proposes a twostep unsupervised learningfree method to segment sparse 3D point cloud with low computational demands. The 3D point cloud data is projected into a lidarspecific 2D coordinate system called lidarimage. For unmanned vehicles, road is a special kind of object, so the algorithm divides road firstly by using improved lidarhistogram. The algorithm further examines the geometric connection between adjacent points in 3D space, thus dividing nonroad points into independent subsets. Experiments on KITTI dataset validate the proposed algorithm outperforms similar methods.