#Pruner-Zero: Machine Learning Framework for Symbolic Pruning Metric Discovery

A new approach in computer vision and graphics addresses the challenge of reconstructing 3D scenes from sparse 2D images by inverting Neural Radiance Fields (NeRFs). Traditional NeRFs are limited to forward rendering tasks, hindering their broader applicability in scenarios like AR, VR, and robotic perception. Current 3D scene reconstruction methods face challenges related to computational complexity, scalability, and data efficiency. The new approach introduces a latent code that captures the 3D structure of a scene, optimizing it to match given 2D observations. This method enables reconstruction from a sparse set of 2D images, enhancing efficiency and accuracy. The approach employs a deep neural network architecture with an encoder, decoder, and differentiable renderer, processing input images to extract features and generate NeRF parameters for synthesizing 2D images. Evaluation results demonstrate improved reconstruction accuracy and computational efficiency, making the method suitable for real-time applications and scalable deployments. This research contributes significantly to the field of AI by advancing 3D scene understanding and opening new possibilities for applications in AR, VR, and robotic perception. The approach offers a more efficient and accurate solution compared to existing methods, showcasing its potential impact on various domains requiring real-time and scalable 3D understanding.

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Source link: https://www.marktechpost.com/2024/07/01/pruner-zero-a-machine-learning-framework-for-symbolic-pruning-metric-discovery-for-large-language-models-llms/?amp