More Like this

1. Deep Depth Estimation from Visual-Inertial SLAM

   https://doi.org/10.1109/IROS45743.2020.9341448  

   Sartipi, Kourosh ; Do, Tien ; Ke, Tong ; Vuong, Khiem ; Roumeliotis, Stergios I. ( October 2020 , 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper addresses the problem of learning to complete a scene's depth from sparse depth points and images of indoor scenes. Specifically, we study the case in which the sparse depth is computed from a visual-inertial simultaneous localization and mapping (VI-SLAM) system. The resulting point cloud has low density, it is noisy, and has nonuniform spatial distribution, as compared to the input from active depth sensors, e.g., LiDAR or Kinect. Since the VI-SLAM produces point clouds only over textured areas, we compensate for the missing depth of the low-texture surfaces by leveraging their planar structures and their surface normals which is an important intermediate representation. The pre-trained surface normal network, however, suffers from large performance degradation when there is a significant difference in the viewing direction (especially the roll angle) of the test image as compared to the trained ones. To address this limitation, we use the available gravity estimate from the VI-SLAM to warp the input image to the orientation prevailing in the training dataset. This results in a significant performance gain for the surface normal estimate, and thus the dense depth estimates. Finally, we show that our method outperforms other state-of-the-art approaches both on training (ScanNet [1] andmore »NYUv2 [2]) and testing (collected with Azure Kinect [3]) datasets.« less
2. Balanced centroidal power diagrams for redistricting

   https://doi.org/10.1145/3274895.3274979  

   Cohen-Addad, Vincent ; Klein, Philip N. ; Young, Neal E. ( November 2018 , Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems)

   We consider the problem of political redistricting: given the locations of people in a geographical area (e.g. a US state), the goal is to decompose the area into subareas, called districts, so that the populations of the districts are as close as possible and the districts are ``compact'' and ``contiguous,'' to use the terms referred to in most US state constitutions and/or US Supreme Court rulings. We study a method that outputs a solution in which each district is the intersection of a convex polygon with the geographical area. The average number of sides per polygon is less than six. The polygons tend to be quite compact. Every two districts differ in population by at most one (so we call the solution balanced). In fact, the solution is a centroidal power diagram: each polygon has an associated center in ℝ³ such that * the projection of the center onto the plane z = 0 is the centroid of the locations of people assigned to the polygon, and * for each person assigned to that polygon, the polygon's center is closest among all centers. The polygons are convex because they are the intersections of 3D Voronoi cells with the plane. Themore »solution is, in a well-defined sense, a locally optimal solution to the problem of choosing centers in the plane and choosing an assignment of people to those 2-d centers so as to minimize the sum of squared distances subject to the assignment being balanced. * A practical problem with this approach is that, in real-world redistricting, exact locations of people are unknown. Instead, the input consists of polygons (census blocks) and associated populations. A real redistricting must not split census blocks. We therefore propose a second phase that perturbs the solution slightly so it does not split census blocks. In our experiments, the second phase achieves this while preserving perfect population balance. The district polygons are no longer convex at the fine scale because their boundaries must follow the boundaries of census blocks, but at a coarse scale they preserve the shape of the original polygons.« less
3. 3D Point Cloud Generation with Millimeter-Wave Radar

   https://doi.org/10.1145/3432221  

   Qian, Kun ; He, Zhaoyuan ; Zhang, Xinyu ( December 2020 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Emerging autonomous driving systems require reliable perception of 3D surroundings. Unfortunately, current mainstream perception modalities, i.e., camera and Lidar, are vulnerable under challenging lighting and weather conditions. On the other hand, despite their all-weather operations, today's vehicle Radars are limited to location and speed detection. In this paper, we introduce MILLIPOINT, a practical system that advances the Radar sensing capability to generate 3D point clouds. The key design principle of MILLIPOINT lies in enabling synthetic aperture radar (SAR) imaging on low-cost commodity vehicle Radars. To this end, MILLIPOINT models the relation between signal variations and Radar movement, and enables self-tracking of Radar at wavelength-scale precision, thus realize coherent spatial sampling. Furthermore, MILLIPOINT solves the unique problem of specular reflection, by properly focusing on the targets with post-imaging processing. It also exploits the Radar's built-in antenna array to estimate the height of reflecting points, and eventually generate 3D point clouds. We have implemented MILLIPOINT on a commodity vehicle Radar. Our evaluation results show that MILLIPOINT effectively combats motion errors and specular reflections, and can construct 3D point clouds with much higher density and resolution compared with the existing vehicle Radar solutions.
4. Polygon-based computer-generated holography: a review of fundamentals and recent progress [Invited]

   https://doi.org/10.1364/AO.444973  

   Zhang, Yaping ; Fan, Houxin ; Wang, Fan ; Gu, Xianfeng ; Qian, Xiaofan ; Poon, Ting-Chung ( January 2022 , Applied Optics)

   In this review paper, we first provide comprehensive tutorials on two classical methods of polygon-based computer-generated holography: the traditional method (also called the fast-Fourier-transform-based method) and the analytical method. Indeed, other modern polygon-based methods build on the idea of the two methods. We will then present some selective methods with recent developments and progress and compare their computational reconstructions in terms of calculation speed and image quality, among other things. Finally, we discuss and propose a fast analytical method called the fast 3D affine transformation method, and based on the method, we present a numerical reconstruction of a computer-generated hologram (CGH) of a 3D surface consisting of 49,272 processed polygons of the face of a real person without the use of graphic processing units; to the best of our knowledge, this represents a state-of-the-art numerical result in polygon-based computed-generated holography. Finally, we also show optical reconstructions of such a CGH and another CGH of the Stanford bunny of 59,996 polygons with 31,724 processed polygons after back-face culling. We hope that this paper will bring out some of the essence of polygon-based computer-generated holography and provide some insights for future research.
5. Assessing Potential Safety Zone Suitability Using a New Online Mapping Tool

   https://doi.org/10.3390/fire5010005  

   Campbell, Michael J. ; Dennison, Philip E. ; Thompson, Matthew P. ; Butler, Bret W. ( February 2022 , Fire)

   Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation ofmore »SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use.« less