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[SIGGRAPH Asia '25] Editable Physically-based Reflections in Raytraced Gaussian Radiance Fields Contributions: • A reconstruction method for radiance fields, with distinct optimization for diffuse and specular components, using path tracing for the latter. • An efficient and accurate training method that reconstructs the diffuse and specular components of the... show more

MrNeRF

16,940 subscribers

15,866 Aufrufe • vor 1 Jahr •via X (Twitter)

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4 Kommentare

Profilbild von MrNeRF

MrNeRFvor 1 Jahr

Paper (pdf): Project: YouTube:

Profilbild von Dozer🚜

Dozer🚜vor 1 Jahr

Oh wow, would love to check out how they are instantiating individual objects in the environment to allow for that kind of part-wise duplication

Profilbild von MrNeRF

MrNeRFvor 1 Jahr

They look very clean, indeed.

Profilbild von Sreerag Raghavan

Sreerag Raghavanvor 1 Jahr

We’re getting closer and closer to simulated reality. This looks incredible.

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Check out our fun work on robust view synthesis! 🤩 Using a casual video as input, our method jointly 1⃣estimates accurate camera poses and 2⃣reconstructs multiple local radiance fields for a large scene. Project🕸️:

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[SIGGRAPH Asia '24 (TOG)] Representing Long Volumetric Video with Temporal Gaussian Hierarchy Contributions: • We introduce a novel, efficient, and expressive Temporal Gaussian Hierarchy representation for long volumetric video. To our knowledge, our method is the first approach capable of handling minutes of volumetric video data. • We propose a Compact Appearance Model and a new rasterization implementation to facilitate real-time, high-quality dynamic view synthesis while maintaining a compact size. • We propose a system to efficiently model long volumetric videos for the first time and demonstrate state-of-the-art dynamic view synthesis quality on the Neural3DV [Li et al. 2022], ENeRF-Outdoor [Lin et al. 2022], and MobileStage [Xu et al. 2024b] datasets, while also achieving the best rendering speed with reduced training cost and memory usage.

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The biggest announcement from NVIDIA GTC was just released! It's the open sourcing of TWO Radiance Field methods, 3D Gaussian Ray Tracing and 3D Gaussian Unscented Transform from NVIDIA AI Developer. Not only has the code been released, but they come with an Apache 2.0 License, meaning you can use these methods commercially. This is the same license that Nerfstudio carries and is a big win for the community! 3DGRT introduces a pure ray tracing implementation, while 3DGUT brings the effects of ray tracing, such as secondary lighting effects to rasterization. This is a big deal! Article👇 Code👇

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Painting with 3D Gaussian Splat Brushes Contributions: • Designing a set of interactive tools and brush parameters for artistic brush creation and control using 3DGS content. • Computing oriented 3D Gaussian splat brushes for stamp-based painting on 3D surfaces represented as meshes or 3DGS scenes. • Deforming the splats in a brush stamp to ensure a smooth appearance of the painted 3D stroke. • Producing seamless brush strokes despite overlapping brush stamps, using diffusion inpainting. • Efficient modeling and rendering of brush strokes to facilitate 3DGS painting in real time.

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Learn how the Radiance Field method, Gaussian Splatting gets compressed with @wimost and Florian Barthel's Self Organizing Gaussians. Self Organizing Gaussians is also what nerfstudio uses for gsplat. Full episode is now live here:

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[SIGGRAPH '25] Monocular Online Reconstruction with Enhanced Detail Preservation Abstract (excerpt): Our approach addresses two key challenges in monocular online reconstruction: 1. Distributing Gaussians without relying on depth maps. 2. Ensuring both local and global consistency in the reconstructed maps. To achieve this, we introduce two key modules: - Hierarchical Gaussian Management Module: For effective Gaussian distribution. - Global Consistency Optimization Module: For maintaining alignment and coherence at all scales. In addition, we present the Multi-level Occupancy Hash Voxels (MOHV), a structure that regularizes Gaussians to capture details across multiple levels of granularity. MOHV ensures accurate reconstruction of both fine and coarse geometries and textures, preserving intricate details while maintaining overall structural integrity. Compared to state-of-the-art RGB-only and even RGB-D methods, our framework achieves superior reconstruction quality with high computational efficiency.

[SIGGRAPH '25] Monocular Online Reconstruction with Enhanced Detail Preservation Abstract (excerpt): Our approach addresses two key challenges in monocular online reconstruction: 1. Distributing Gaussians without relying on depth maps. 2. Ensuring both local and global consistency in the reconstructed maps. To achieve this, we introduce two key modules: - Hierarchical Gaussian Management Module: For effective Gaussian distribution. - Global Consistency Optimization Module: For maintaining alignment and coherence at all scales. In addition, we present the Multi-level Occupancy Hash Voxels (MOHV), a structure that regularizes Gaussians to capture details across multiple levels of granularity. MOHV ensures accurate reconstruction of both fine and coarse geometries and textures, preserving intricate details while maintaining overall structural integrity. Compared to state-of-the-art RGB-only and even RGB-D methods, our framework achieves superior reconstruction quality with high computational efficiency.

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