NeRF represents scenes as continuous 3D volumes. Instead of discrete 3D meshes or point clouds, it defines a function that calculates color and density values for any 3D point within the scene. By training the neural network on multiple scene images captured from different viewpoints, NeRF learns to generate consistent and accurate representations that align with the observed images.
Once the NeRF model is trained, it can synthesize new photorealistic views of the scene from arbitrary camera viewpoints, creating high-quality rendered images. NeRF aims to capture high-fidelity scene details, including complex lighting effects, reflections, and transparencies, which can be challenging for traditional 3D reconstruction methods.
NeRF has shown promising results in generating high-quality 3D reconstructions and representing novel views of scenes, making it useful for applications in computer graphics, virtual reality, augmented reality, and other fields where accurate representations of 3D scenes They are essential. However, NeRF also faces computational challenges due to its significant memory and processing power requirements, especially for capturing large and detailed scenes.
3D Gaussian scattering involves a substantial number of 3D Gaussians to maintain high fidelity of rendered images, which requires a large amount of memory and storage. Reducing the number of Gaussian points without sacrificing performance and compressing Gaussian attributes increases efficiency. Researchers at Sungkyunkwan University propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance.
They also propose a compact but efficient view-dependent color representation using a grid-based neural field instead of relying on spherical harmonics. Their work provides a comprehensive framework for rendering 3D scenes, achieving high performance, fast training, compactness, and real-time rendering.
They have extensively tested compact 3D Gaussian representation on various data sets, including real and synthetic scenes. Throughout the experiments, regardless of the data set, they consistently found ten times lower storage and improved rendering speed while maintaining scene rendering quality compared to 3D Gaussian Splatting.
Point-based methods have been widely used to render 3D scenes. The simplest form is point clouds. However, point clouds can cause visual artifacts such as holes and aliasing. Researchers proposed point-based neural representation methods to mitigate this by processing points using rasterization-based point scattering and differentiable rasterization.
The future of NeRF promises to revolutionize the understanding and representation of 3D scenes, and ongoing research efforts are expected to further push the boundaries, enabling more efficient, realistic, and versatile applications in various domains.
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Arshad is an intern at MarktechPost. He is currently pursuing his international career. Master's degree in Physics from the Indian Institute of technology Kharagpur. Understanding things down to the fundamental level leads to new discoveries that lead to the advancement of technology. He is passionate about understanding nature fundamentally with the help of tools such as mathematical models, machine learning models, and artificial intelligence.
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