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What is SURE Aerial?

nFrames SURE software presents a robust solution for dense image surface reconstruction, specifically designed for entities engaged in mapping, surveying, geo-information, and research activities. This innovative software is proficient in producing precise point clouds, Digital Surface Models (DSMs), True Orthophotos, and textured meshes from images of various dimensions, including small, medium, and large formats. It is exceptionally useful for a wide array of applications, such as national mapping projects, monitoring tasks that employ both manned aircraft and UAVs, as well as cadaster, infrastructure planning, and 3D modeling. SURE Aerial is tailored for aerial image datasets sourced from large frame nadir cameras, oblique cameras, and hybrid systems that incorporate additional LiDAR sensors. The software adeptly manages images at any resolution, enabling the creation of 3D meshes, True Orthophotos, point clouds, and DSMs on standard workstation hardware or within clustered computing environments. Notably, it boasts a user-friendly interface, straightforward setup processes, and adherence to industry standards for mapping, which enhances its compatibility with technologies that facilitate web streaming. Its flexibility guarantees that it caters to the varied requirements of multiple projects while ensuring dependable results, thereby solidifying its role as an essential tool in modern geospatial workflows.

What is GET3D?

We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

nFrames

Date Founded

2014

Company Location

Germany

Company Website

www.nframes.com

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Categories and Features

GIS

3D Imagery
Census Data Integration
Color Coding
Geocoding
Image Exporting
Image Management
Internet Mapping
Interoperability
Labeling
Map Creation
Map Sharing
Near-Matching
Reverse Geocoding
Spatial Analysis

Categories and Features

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