SBIR Phase I: Reconstructing Consistently Detailed City-Scale Environments From Incomplete 2D and 3D Data

Period of Performance: 07/01/2017 - 06/30/2018

$224K

Phase 1 SBIR

Recipient Firm

Geopipe, Inc.
19 E 7th St Apt 2 Array
New York, NY 10003
Firm POC, Principal Investigator

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to make it cheaper and faster for architects, urban planners, and real-estate developers (APDs), as well as many others, to work with detailed models of the real world. Designers in APD fields must visualize and render their projects in the context of the real world. Pictures, videos, 3D printing, and even virtual reality inform the design process and facilitate communication with lay customers and stakeholders. These applications require consistently detailed models of the built world, and this project will automate the generation of these models. We estimate that APDs spend at least $80M annually creating these models by hand; and that at least $300M more is spent on such models for simulations, special effects, and video game design. By algorithmically generating virtual models without human intervention, the significant cost (in time and money) of manual creation will be saved, freeing design professionals to do work they want to be doing. This Small Business Innovation Research (SBIR) Phase I project will advance the state of the art in reconstructing highly detailed models of the world for diverse commercial applications. The first hurdle is solving the problem of reconstructing surfaces representing the boundaries of real-world solids (buildings) from noisy point cloud data. While surface reconstruction is well-studied in a variety of contexts, it remains an open problem in general, as successful algorithms must be informed by priors on the intended datasets. Using a data-driven approach to segment and classify input point clouds will facilitate the application of different reconstruction techniques to different objects (e.g. trees or buildings). The second hurdle is development of a machine learning algorithm which handles the dual problems of procedural modeling and inverse procedural modeling from a single statistical model, enabling visually realistic predictions about the details of a given building, even when that information is not available from source data (which may be of inconsistent quality across a large geographic area).