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GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as... an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.show more

MrNeRF

16,808 subscribers

17,786 Aufrufe • vor 1 Jahr •via X (Twitter)

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6 Kommentare

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MrNeRFvor 1 Jahr

Paper (link to pdf): Project:

Profilbild von α

αvor 1 Jahr

Spectacular.

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Sam Duttervor 1 Jahr

Wow! 90 fps is impressive. Is this something that could be implemented in an engine like Unity? I'd love to play with it!

Profilbild von TheSeanLavery

TheSeanLaveryvor 1 Jahr

Honestly, you are one of my favorite accounts to view posts from. You give me a true glimpse into the future. :)

Profilbild von MrNeRF

MrNeRFvor 1 Jahr

Thank you! I really appreciate it!

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Oleg Pavlovvor 1 Jahr

That's impressive. How is performance on consumer hardware if you have a hundred of these on screen?

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Whilst we figure out a solution for temporal filtering on 3D Gaussian Splatting input frames. We can use a temporal filtering plugin on the screen captures or rendered images to act as a visualization tool to check what the future results might look like. "Processing and rendering a digital human with 3D Gaussian Splatting with a post process to simulate temporal filtering." Left video no temporal filtering. Right video with temporal filtering. R&D for archviz projects. #GaussianSplatting #ir #inria #sibr #aftereffects #temporalfiltering

Whilst we figure out a solution for temporal filtering on 3D Gaussian Splatting input frames. We can use a temporal filtering plugin on the screen captures or rendered images to act as a visualization tool to check what the future results might look like. "Processing and rendering a digital human with 3D Gaussian Splatting with a post process to simulate temporal filtering." Left video no temporal filtering. Right video with temporal filtering. R&D for archviz projects. #GaussianSplatting #ir #inria #sibr #aftereffects #temporalfiltering

Infinite-Realities

58,368 Aufrufe • vor 2 Jahren

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MaterialFusion Enhancing Inverse Rendering with Material Diffusion Priors discuss: Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.

AK

22,959 Aufrufe • vor 1 Jahr

📢Happy to present Convex Splatting, a novel way for 3D reconstruction based on 3D smooth convexes. For the first time, a splatting-based method reaches the quality of NeRF sota methods but with real-time rendering and few primitives!! I expect this to replace Gaussian splatting for 3D in the coming months. CODE RELEASED TODAY! joint work with collaborators from Université de Liège Visual Geometry Group (VGG) , KAUST Computer Vision Lab (IVUL) a thread 🧵 1/n

📢Happy to present Convex Splatting, a novel way for 3D reconstruction based on 3D smooth convexes. For the first time, a splatting-based method reaches the quality of NeRF sota methods but with real-time rendering and few primitives!! I expect this to replace Gaussian splatting for 3D in the coming months. CODE RELEASED TODAY! joint work with collaborators from Université de Liège Visual Geometry Group (VGG) , KAUST Computer Vision Lab (IVUL) a thread 🧵 1/n

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