Machine Learning Art
3D from 2D in the Blink of an AI
GARF state-of-the-art in reconstruction and pose estimation
Can you convert a 2D image to 3D?
Machine Learning can almost instantly construct a 3D scene. The authors have created a new method to generate 3D environments from a single 2D image. GARF was born. Can optimize for high-quality representation of scenes from unknown camera positions without auxiliary position planning.
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What is a NeRF neural network, and how does it work?
The Neural Radiance Field, or NeRF, is a technique for creating new perspectives on complicated situations. NeRF takes a group of input photos from a scene and interpolates between them to render the entire scene.
an example of NeRF — Render 3D Scene (colab)
Even though NeRF has shown promising outcomes in creating photorealistic new views of real-world situations, most present techniques need precise previous camera postures. Although there are methods for recovering the radiance field and camera posture simultaneously (BARF), they rely on a time-consuming coarse-to-fine auxiliary positional embedding to get satisfactory results. The authors offer Gaussian Activated Neural Radiance Fields GARF, a novel positional embedding-free neural radiance field architecture that exceeds the state-of-the-art in terms of high fidelity reconstruction and poses estimation by applying Gaussian activations.
GARF is compared against several network topologies, including PE-MLP, BARF, SIREN. GARF solves for exact geometric transformations and neural picture representation using gaussian activations instead of cumbersome multi-dimensional parameter adjustment and model initialization.
The new method is extremely precise and devoid of the disadvantages of previous solutions, such as artifacts visible in the below photos.
The left scene was captured using iPhone. Top row: Rendered image and depth scene using an old method Bottom row: Rendered image and depth scene using GARF.
robots and self-driving cars
By collecting 2D images or videotape footage of real-world things, the method might be used to educate robots and self-driving cars to grasp their size and shape. It might also be used in architecture and entertainment to quickly build digital replicas of real-world terrains that artists can rework and add to. The new method GARF makes it more efficiency.
title: GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
the authors :Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, Simon Lucey
Poject page:
https://arxiv.org/pdf/2204.05735.pdf
Colab: an example of NeRF:
Keywords: 3D, computer vision, Artificial Intelligence, Graphics, Machine Learning, AI art, art, digital art, GARF, AR, VR, inverse graphics, Pattern Recognition, NeRF
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