In the digital transformation era, how we perceive and interact with our surroundings is significantly shifting. The advent of Neural Radiance Fields (NeRF) has opened up new possibilities in the realm of 3D reconstruction and novel view synthesis.
Let’s dive into the application of NeRF in reconstructing indoor spaces, a groundbreaking initiative by Google Research.
The Power of Immersive Experiences
When it comes to choosing a venue, be it a restaurant for a date or a café for a casual meet-up, we often find ourselves pondering over questions like the ambience of the place, the availability of outdoor seating, or the number of screens to watch a game. While photos and videos can provide a glimpse, they can’t replace the experience of being there in person.
This is where immersive experiences come into play. Interactive, photorealistic, and multi-dimensional, these experiences bridge the gap between reality and virtuality, recreating the feel and vibe of a space. Google Maps’ Immersive View is a prime example of this, using machine learning and computer vision to fuse billions of Street View and aerial images to create a rich, digital model of the world.
The Magic of Neural Radiance Fields (NeRF)
NeRF, or Neural Radiance Fields, is a state-of-the-art approach for fusing photos to produce a realistic, multi-dimensional reconstruction within a neural network. Given a collection of photos describing a scene, NeRF distils these photos into a neural field, which can then be used to render photos from viewpoints not present in the original collection.
The Process: From Photos to Neural Radiance Fields (NeRFs)
The first step to producing a high-quality NeRF is carefully capturing a scene: a dense collection of photos from which 3D geometry and colour can be derived. To obtain the best possible reconstruction quality, every surface should be observed from multiple different directions.
Once the capture is uploaded to the system, processing begins. As photos may inadvertently contain sensitive information, they are automatically scanned and blurred to remove personally identifiable content. A structure-from-motion pipeline is then applied to solve each photo’s camera parameters: its position and orientation relative to other photos, along with lens properties like focal length.
NeRF Reconstruction
Unlike many ML models, a new NeRF model is trained from scratch on each captured location. To obtain the best possible reconstruction quality within a target compute budget, features from various published works on Neural Radiance Fields (NeRF) developed at Alphabet are incorporated. Some of these include:
- Building on mip-NeRF 360, one of the best-performing NeRF models to date.
- Incorporating the low-dimensional generative latent optimization (GLO) vectors introduced in NeRF in the Wild as an auxiliary input to the model’s radiance network.
- Incorporating exposure conditioning as introduced in Block-NeRF.
Delivering a Scalable User Experience
Once a NeRF is trained, new photos of a scene can be produced from any viewpoint and camera lens. The goal is to deliver a meaningful and helpful user experience: not only the reconstructions themselves but guided, interactive tours that give users the freedom to explore spaces from the comfort of their smartphones naturally.
Open Research Questions
While this feature marks a significant step towards universally accessible, AI-powered, immersive experiences, more questions remain open. These include enhancing reconstructions with scene segmentation, adapting NeRF to outdoor photo collections, and enabling real-time, interactive 3D exploration through neural rendering on the device.
Bottom Line
The application of NeRF in reconstructing indoor spaces marks a significant milestone in the realm of 3D reconstruction and novel view synthesis. As we continue to grow, we look forward to engaging with and contributing to the community to build the next generation of immersive experiences.
FAQs
Q1: What is NeRF?
NeRF, or Neural Radiance Fields, is a state-of-the-art approach for fusing photos to produce a realistic, multi-dimensional reconstruction within a neural network. Given a collection of photos describing a scene, NeRF distils these photos into a neural field, which can then be used to render photos from viewpoints not present in the original collection.
Q2: How does Google use NeRF for indoor space reconstruction?
Google uses NeRF to create immersive experiences for users. They capture a dense collection of photos of a scene, process them to remove sensitive information and solve for each photo’s camera parameters. A new NeRF model is trained from scratch on each captured location, incorporating features from various published works on NeRF. The trained NeRF can then produce new photos of a scene from any viewpoint and camera lens.
Q3: What are the potential future improvements in this field?
Future improvements in this field may include enhancing reconstructions with scene segmentation, adapting NeRF to outdoor photo collections, and enabling real-time, interactive 3D exploration through neural rendering on the device.
Q4: What is the purpose of Google’s Immersive View?
Google Maps’ Immersive View uses machine learning and computer vision to fuse billions of Street View and aerial images to create a rich, digital model of the world. It provides indoor views of restaurants, cafes, and other venues to give users a virtual up-close look that can help them confidently decide where to go.
Q5: How does NeRF contribute to the user experience?
Once a NeRF is trained, it can produce new photos of a scene from any viewpoint and camera lens. This allows for the creation of guided, interactive tours that give users the freedom to explore spaces from the comfort of their smartphones naturally.
