PDF-Realtime D Reconstruction at Scale using Voxel Hashing Matthias Niener Michael Zollh ofer

Scene is about 20 wide and high and captured online in less than 5 minutes with live feedback of the reconstruction Abstract Online 3D reconstruction is gaining newfound interest due to the availability of realtime consumer depth cameras The basic p. c d Simple hardware modi64257cations allow active illuminated near infrared images to be captured from the camera e f This is used as input into our machine learning algorithm for depth estimation g h Our algorithm outputs dense metric depth maps of FF can be used for general signal restoration tasks that can be tackled via convolutional 64257lter ing where it attempts to learn the optimal 64257ltering kernels to be applied to each data point The model can learn both the size of the kernel and In this work we extend the technique to explicitly incorporate the points as interpolation constraints The extension can be interpreted as a generalization of the underlying mathematical framework to a screened Poisson equation In contrast to other Recently bit minwise hashing has been applied to largescale learning and sublinear time near neighbor search The major drawback of minwise hashing is the expensive pre processing as the method requires applying eg 200 to 500 permutations on the dat Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th PieterAbbeelpabbeel@cs.stanford.eduMorganQuigleymquigley@cs.stanford.eduAndrewY.Ngang@cs.stanford.eduComputerScienceDepartment,StanfordUniversity,Stanford,CA94305,USAAbstractInthemodel-basedpolicysear COL 106. Shweta Agrawal, . Amit. Kumar. Slide Courtesy : Linda Shapiro, . Uwash. Douglas W. Harder, . UWaterloo. 12/26/03. Hashing - Lecture 10. 2. The Need for Speed. Data structures we have looked at so far. Yunchao. Gong. UNC Chapel Hill. yunchao@cs.unc.edu. The problem. Large scale image search:. We have a candidate image. Want to search a . large database . to find similar images. Search the . internet. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . Plan. I spent the last decade advising on numerous cases where hash tables/functions were used. A few observations on . What data structures I’ve seen implemented and where. What do developers think, were they need help. In static hashing, function . h. maps search-key values to a fixed set of . B. . buckets, that contain a number of (K,V) entries.. . . Problem: d. atabases . grow . (or shrink) . with time. . If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows.. Naifan Zhuang, Jun Ye, Kien A. Hua. Department of Computer Science. University of Central Florida. ICPR 2016. Presented by Naifan Zhuang. Motivation and Background. According to a report from Cisco, by 2019:. After Civil War came Reconstruction The two stage policy Presidential reconstruction, and congressional reconstruction Aimed to rebuild and strengthen the country But what did this mean for Native Americans The Power of Theories of Change Copyright 2010 by Leland Stanford Jr University All Rights Reserved STANFORD SOCIAL NNOVATION 0154010I0f0h00d0010000