Given a set of a reference RGBD views of an indoor environment, and a new viewpoint, our goal is to predict the view from that location.

Neural Information Processing Systems http://nips.cc/

Automatic relevance determination (ARD), and the closely-related sparse Bayesian learning (SBL) framework, are effective tools for pruning large numbers of irrelevant features. However, popular update rules used for this process are either prohibitively slow in practice and/or heuristic in nature without proven convergence properties. This paper furnishes an alternative means of optimizing a general ARD cost function using an auxiliary function that can naturally be solved using a series of re-weighted L1 problems. The result is an efficient algorithm that can be implemented using standard convex programming toolboxes and is guaranteed to converge to a stationary point unlike existing methods. The analysis also leads to additional insights into the behavior of previous ARD updates as well as the ARD cost function.

Have you ever imagined being able to take a picture and just magically dive into it as if it would be a door to another world? Well, whether you thought about this or not, some people did, and thanks to them, it is now possible with AI! This is just one step away from teleportation and being able to be there physically. Maybe one day AI will help with that and fix an actual problem too! I'm just kidding, this is really cool, and I'm glad some people are working on it.

One of the benefits of being proficient with Machine Learning is having a good understanding of the algorithms that run some of the wonderful features we see on our devices. When Apple, the computer device manufacturing company, released the iOS16 version, one of the new functionalities was the ability to use the default Notes app as a digital scanner, think of it as a "scanner in your palm", borrowing a similar phrase from the legendary Steve Jobs. Prior to when it was introduced, I had to use other services usually apps downloaded from the App Store for the purpose of scanning documents with my phones, some paid some free and some of the free apps come with the disadvantage of a watermark which somewhat defeats the purpose unless you subscribe to a paid version. Having worked on a number of computer vision projects, I thought, would it be possible there is some computer vision library or ML algorithm one can use to replicate what's been done in my phone? In this article, we will be using a very popular library familiar to most MLEs familiar with deep learning particular computer vision: OpenCV.

The deep learning era began through the advancements it brought in traditional 2D image-recognition tasks such as classifications, detections, and instance segmentations. As the techniques matured, the research in deep-learning-based computer vision has been shifted towards fundamental 3D computer vision problems -- one of the most notable being synthesising new views of an object and reconstructing the 3D shape of it from images. Many approaches tackled this as a conventional machine learning problem, where the goal becomes to learn a system to "inflate" 3D geometry out of images after a finite set of training iterations. Recently, however, a completely new direction, namely Neural Radiance Fields (NeRF), has been introduced. This article dives into the basic concepts of the originally proposed NeRF as well as several of its extensions in recent years.

GCP offers a suite of cloud technologies with fully managed and serverless solutions that make processing, storing, and analyzing data easy. This analysis will utilize BigQuery, Geo Viz, and AI platform. GCP offers $300 of trial credits to new users. Additionally, BigQuery comes with 1TB of free processing a month. This demo uses a fraction of these credits.

This article introduces Nautilus' month-long exploration of the science and art of time. When Lee Smolin's book Time Reborn was re-launched to great fanfare at my home in 2015, it accelerated a discussion he and I had been having for years: What if we gathered together a group of creative, broad-thinking artists and scientists whose perspectives on time were directly reflected in their work? What an extraordinary opportunity that would be--to journey down the path of time hand-in-hand with people whose work has literally changed the way we see and hear things. So that's what we did. Lee's remarkable ability to think about time in a both a fundamental and historical context, and to communicate his ideas to a broad audience, informed our choice of writers and the direction we gave them.

For instance, any of the utility company wants to have a close look of the remote power lines, the Airbus aerial satellites would help pull off data and for the closer look; it might contract with a local company to run a plane or drone flight over the area. Lately, Airbus announced its partnership with DroneBase for better results and inspection. Airbus has also started mapping the runways at Atlanta's Hartsfield-Jackson, the world's busiest airports. Here, the company used Sensefly's fixed-wing drone which flies autonomously, capturing images of the ground, and then the results are checked and complied, including 3D maps to show bumps and cracks, and GPS data to locate busted lights. "In the coming time, drones would be taking up certain projects that would make a great impact," says FAA.

You may know Airbus as that Boeing competitor that also makes planes, but the European company is in fact an defense and aerospace giant that makes helicopters, satellites, and drones, and now it's using its aircraft not just to move people, but to give those on the ground a whole new view from the skies. A year-old effort called Airbus Aerial will seek to serve climate modelers, farmers, city planners, engineers, first responders, and anybody else who needs a a particular view of the world. The company combines data from observation satellites (of which Airbus is the largest global operator), manned planes with cameras slung underneath, and drones, to get to the places others can't reach. Airbus Aerial packages it all up, and presents it neatly to the customer, via a cloud-based interface. "It's a very complex thing to just say'I need satellite data'," says Jesse Kallman, president of the company.