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The first public dynamic Gaussian Splatting implementation in VR is here from San Francisco based Gmix, compatible with WebXR and Apple Vision Pro. 🔗

Radiance Fields

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31,612 Aufrufe • vor 2 Jahren •via X (Twitter)

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splaTV, developed by Kevin Kwok, is a browser-based implementation for experiencing dynamic Gaussian Splatting with Spacetime Gaussians. This newly released application supports both WebGL and WebXR technologies and is available under the MIT License. 🔗

splaTV, developed by Kevin Kwok, is a browser-based implementation for experiencing dynamic Gaussian Splatting with Spacetime Gaussians. This newly released application supports both WebGL and WebXR technologies and is available under the MIT License. 🔗

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MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting Contributions: • MoE-GS: the first dynamic Gaussian splatting framework employing a Mixture-of-Experts architecture, enabling robust and adaptive reconstruction across diverse dynamic scenes. • A novel Volume-aware Pixel Router integrates expert outputs through differentiable weight splatting, achieving spatially and temporally coherent adaptive blending. • Efficiency of MoE-GS is improved through single-pass multi-expert rendering and gate-aware Gaussian pruning. A separate knowledge distillation strategy trains individual experts with pseudo-labels from the MoE model, enhancing quality without modifying the architecture.

MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting Contributions: • MoE-GS: the first dynamic Gaussian splatting framework employing a Mixture-of-Experts architecture, enabling robust and adaptive reconstruction across diverse dynamic scenes. • A novel Volume-aware Pixel Router integrates expert outputs through differentiable weight splatting, achieving spatially and temporally coherent adaptive blending. • Efficiency of MoE-GS is improved through single-pass multi-expert rendering and gate-aware Gaussian pruning. A separate knowledge distillation strategy trains individual experts with pseudo-labels from the MoE model, enhancing quality without modifying the architecture.

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Some personal news! 📣 Recently, I joined Niantic as the product lead to work on MR apps and Gaussian splatting! Today, we’re launching “Into The Scaniverse”—A way of viewing Scaniverse splats in VR (yours and the ones posted to the map) 📸 It’s available as a webxr app via Quest Browser (for Q3 and Q3S), soon to launch in the Meta Horizon store. Honored to be working with quite a lot of brilliant folks at Niantic 🙌 Link to the webxr app 👇

Some personal news! 📣 Recently, I joined Niantic as the product lead to work on MR apps and Gaussian splatting! Today, we’re launching “Into The Scaniverse”—A way of viewing Scaniverse splats in VR (yours and the ones posted to the map) 📸 It’s available as a webxr app via Quest Browser (for Q3 and Q3S), soon to launch in the Meta Horizon store. Honored to be working with quite a lot of brilliant folks at Niantic 🙌 Link to the webxr app 👇

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Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

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