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This is pretty cool 👓 Google Announces Geospatial Creator powered by ARCore & Google Maps and it allows you to visualize, design, and publish world-anchored content by using Unity ! Few features available: 📌 Photorealistic 3D Tiles: Visualize the 3D geometry of the world and deploy location-anchored content accurately... with high-resolution, photorealistic 3D tiles. 📌 Geospatial anchors: Place and anchor 3D content at any given latitude, longitude, and altitude with sub-meter accuracy in areas covered by Google Street View. 📌 Terrain anchors: Place and anchor 3D content with only latitude and longitude coordinates, using data from Google Maps to determine ground level. 📌 Rooftop anchors: Use rooftops to place and anchor 3D content with respect to the building geometry and terrain in areas covered by Google Street View. 📢 To get started with Unity follow these steps: #AR #Googleshow more

Dilmer

49,532 subscribers

58,292 просмотров • 3 лет назад •via X (Twitter)

Образование Наука и технологии Искусство

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Johnny Quest

Johnny Quest3 лет назад

To go along with this

Фото профиля Dilmer Valecillos ᯅ

Dilmer Valecillos ᯅ3 лет назад

I am so happy to see this message thanks for sharing !

Фото профиля Diego Aguirre | AR Developer

Diego Aguirre | AR Developer3 лет назад

Interesting!! - So it will be available to ARCore enabled devices only?

Фото профиля Dilmer Valecillos ᯅ

Dilmer Valecillos ᯅ3 лет назад

It works with iOS and Android, according to their docs you can use the ARCore SDK for iOS.

Фото профиля Diego Aguirre | AR Developer

Diego Aguirre | AR Developer3 лет назад

Thank you for sharing!!

Фото профиля Dilmer Valecillos ᯅ

Dilmer Valecillos ᯅ3 лет назад

You are very welcome Diego, also love your recent prototypes ;)

Фото профиля Jonathan Stephens

Jonathan Stephens3 лет назад

I love how it’s all screen passthrough. It’s tangible today for everyone! No extra hardware needed.

Фото профиля Dilmer Valecillos ᯅ

Dilmer Valecillos ᯅ3 лет назад

Absolutely, this is the beginning of what’s coming with AR headsets, using the world as our canvas !

Фото профиля Frank Bruno

Frank Bruno3 лет назад

So freaking excited about this!

Фото профиля Dilmer Valecillos ᯅ

Dilmer Valecillos ᯅ3 лет назад

I agree 🔥 this is going to enable sooo many possibilities!

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Google Earth

325,130 просмотров • 3 месяцев назад

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135,003 просмотров • 8 месяцев назад

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Unreal Engine

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Qiusheng Wu

24,292 просмотров • 1 месяц назад

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@levelsio

1,488,981 просмотров • 1 год назад

🚀 Big news today with Google + Adobe joining forces! We're talking about 3D content anchored to the real world at insane scale🌐 And of course, AI had a role to play. I've got early access, and let's just say the physical & digital worlds are blurring 😎 Let's get into it!🧵

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Bilawal Sidhu

547,785 просмотров • 3 лет назад

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

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AK

305,663 просмотров • 3 лет назад

Step into the map with the Street View grounding feature in Project Genie from Google DeepMind and Google Labs. Announced at I/O, this research prototype uses locations from Google Maps Street View as a foundation, letting you generate and explore interactive, 360-degree virtual environments from just a text prompt or real-world starting place. Cool, right? But… How does it actually work? 🤔 As an experimental tool, Project Genie tackles the "blank space" problem (showing both what’s in front of the camera and behind it) by utilizing Street View data to realistically generate a 360-degree view of the location you selected as the starting point for your world to generate from. Worlds generated by Genie are far more dynamic and rich because they’re created frame-by-frame based on the world description and user actions. By predicting each subsequent frame, Genie is able to simulate what it looks like to swim across an ocean, or hike to the top of a peak, marking a massive shift in interactive media and simulation pipelines. What real-world place would you want to step into and explore?

Step into the map with the Street View grounding feature in Project Genie from Google DeepMind and Google Labs. Announced at I/O, this research prototype uses locations from Google Maps Street View as a foundation, letting you generate and explore interactive, 360-degree virtual environments from just a text prompt or real-world starting place. Cool, right? But… How does it actually work? 🤔 As an experimental tool, Project Genie tackles the "blank space" problem (showing both what’s in front of the camera and behind it) by utilizing Street View data to realistically generate a 360-degree view of the location you selected as the starting point for your world to generate from. Worlds generated by Genie are far more dynamic and rich because they’re created frame-by-frame based on the world description and user actions. By predicting each subsequent frame, Genie is able to simulate what it looks like to swim across an ocean, or hike to the top of a peak, marking a massive shift in interactive media and simulation pipelines. What real-world place would you want to step into and explore?

Google AI

2,994,968 просмотров • 26 дней назад

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

Shruti

19,832 просмотров • 1 месяц назад

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 просмотров • 2 лет назад

They say the SpaceX IPO will be a rocket. We couldn't wait. So we launched one in the middle of Udine. Thanks to Over the Reality's city-scale 3D mapping and VPS technology, anyone can place and experience massive AR content anchored to the real world with centimeter-level precision. The future isn't on a screen. It's in the world around you.

They say the SpaceX IPO will be a rocket. We couldn't wait. So we launched one in the middle of Udine. Thanks to Over the Reality's city-scale 3D mapping and VPS technology, anyone can place and experience massive AR content anchored to the real world with centimeter-level precision. The future isn't on a screen. It's in the world around you.

Over the Reality 🌐

19,991 просмотров • 1 месяц назад

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Qiusheng Wu

35,726 просмотров • 25 дней назад

Project Genie is a Google Labs experiment that lets you simulate dynamic worlds you can navigate in real time with Genie, our general-purpose world model. Today, we’re connecting Project Genie to nearly 20 years of Street View data from Google Maps — so you can now build interactive spaces based on real-world locations. Street View imagery in Project Genie is available now for places in the U.S., and will expand to more locales over time. #GoogleIO

Project Genie is a Google Labs experiment that lets you simulate dynamic worlds you can navigate in real time with Genie, our general-purpose world model. Today, we’re connecting Project Genie to nearly 20 years of Street View data from Google Maps — so you can now build interactive spaces based on real-world locations. Street View imagery in Project Genie is available now for places in the U.S., and will expand to more locales over time. #GoogleIO

Google

177,069 просмотров • 2 месяцев назад

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

Bilawal Sidhu

27,278 просмотров • 8 месяцев назад

Google has added its flight simulator to the web version of Google Earth, making it available to all users worldwide. >The feature runs directly in any modern browser at with no downloads or installations required. >Users can open the simulator from the Tools menu after selecting Explore and pilot a virtual aircraft over detailed 3D terrain, buildings, and satellite imagery across the entire planet. >It uses simplified flight physics designed for casual exploration and works with standard mouse and keyboard controls.

Google has added its flight simulator to the web version of Google Earth, making it available to all users worldwide. >The feature runs directly in any modern browser at with no downloads or installations required. >Users can open the simulator from the Tools menu after selecting Explore and pilot a virtual aircraft over detailed 3D terrain, buildings, and satellite imagery across the entire planet. >It uses simplified flight physics designed for casual exploration and works with standard mouse and keyboard controls.

Pirat_Nation 🔴

100,095 просмотров • 1 месяц назад

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

MrNeRF

51,761 просмотров • 2 месяцев назад