https://jennyseidenschwarz.github.io/DynOMo.github.io
https://github.com/dvl-tum/DynOMo
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Category: photogrammetry
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Don’t Splat your Gaussians – Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media
https://arcanous98.github.io/projectPages/gaussianVolumes.html
We propose a compact and efficient alternative to existing volumetric representations for rendering such as voxel grids.
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SPAR3D – Stable Point-Aware Reconstruction of 3D Objects from Single Images
SPAR3D is a fast single-image 3D reconstructor with intermediate point cloud generation, which allows for interactive user edits and achieves state-of-the-art performance.
https://github.com/Stability-AI/stable-point-aware-3d
https://stability.ai/news/stable-point-aware-3d?utm_source=x&utm_medium=social&utm_campaign=SPAR3D
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DMesh++ – An Efficient Differentiable Mesh for Complex Shapes
https://sonsang.github.io/dmesh2-project
An efficient differentiable mesh-based method that can effectively handle complex 2D and 3D shapes. For instance, it can be used for reconstructing complex shapes from point clouds and multi-view images.
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Gaussian Splatting OFX plugin for Nuke
https://radiancefields.com/gaussian-splatting-in-nuke
https://aescripts.com/gaussian-splatting-for-nuke
Features
- Import .ply files in Nuke.
- Support Compressed .ply files from SuperSplat
- Crop with Spherical or Box shape.
- Crop with Y Plane.
- Combine up to 10 models in the scene.
- Colorize with Ramp using Spherical or Box shape.
- Reveal model with Opacity Ramp.
- Animate Splat Scale with Spherical or Box shape.
- Each model can be distorted with Noise.
- Render Depth Pass for 3D compose.
- Color correction for each model.
- Real-time with GPU
- Export scene
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Odyssey.systems Explorer – pioneering generative world models and gaussian splatting
Odyssey and a Pixar co-founder, Ed Catmull (also an investor), just dropped Explorer, a revolutionary 3D world generator that turns any image into an editable 3D world.
https://odyssey.systems/introducing-explorer
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Momentum-GS – Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction
https://jixuan-fan.github.io/Momentum-GS_Page
https://github.com/Jixuan-Fan/Momentum-GS
A novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count.
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NoPoSplat – Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
A feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from unposed sparse multi-view images.
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Niantic SPZ – open source, compressed gaussian splats file format
https://scaniverse.com/news/spz-gaussian-splat-open-source-file-format
https://github.com/nianticlabs/spz
• Slashes file sizes by 90% (250MB → 25MB) with virtually zero quality loss
• Lightning-fast uploads/downloads, especially on mobile
• Dramatically reduced memory footprint
• Enables real-time processing right on your phoneTech breakthrough:
• Smart compression of position, rotation, color & scale data
• Column-based organization for maximum efficiency
• Innovative fixed-point quantization & log encodinghttps://www.8thwall.com/products/niantic-studio
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Apple releases Depth Pro – An open source AI model that rewrites the rules of 3D vision
The model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.
https://github.com/apple/ml-depth-pro
https://arxiv.org/pdf/2410.02073
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EVER (Exact Volumetric Ellipsoid Rendering) – Gaussian splatting alternative
https://radiancefields.com/how-ever-(exact-volumetric-ellipsoid-rendering)-does-this-work
https://half-potato.gitlab.io/posts/ever/
Unlike previous methods like Gaussian Splatting, EVER leverages ellipsoids instead of Gaussians and uses Ray Tracing instead of Rasterization. This shift eliminates artifacts like popping and blending inconsistencies, offering sharper and more accurate renderings.
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Meta Horizon Hyperscape
𝐌𝐞𝐭𝐚 𝐇𝐲𝐩𝐞𝐫𝐬𝐜𝐚𝐩𝐞 𝐢𝐧 𝐚 𝐧𝐮𝐭𝐬𝐡𝐞𝐥𝐥
Hyperscape technology allows us to scan spaces with just a phone and create photorealistic replicas of the physical world with high fidelity. You can experience these digital replicas on the Quest 3 or on the just announced Quest 3S.https://www.youtube.com/clip/UgkxGlXM3v93kLg1D9qjJIKmvIYW-vHvdbd0
𝐇𝐢𝐠𝐡 𝐅𝐢𝐝𝐞𝐥𝐢𝐭𝐲 𝐄𝐧𝐚𝐛𝐥𝐞𝐬 𝐚 𝐍𝐞𝐰 𝐒𝐞𝐧𝐬𝐞 𝐨𝐟 𝐏𝐫𝐞𝐬𝐞𝐧𝐜𝐞
This level of photorealism will enable a new way to be together, where spaces look, sound, and feel like you are physically there.
𝐒𝐢𝐦𝐩𝐥𝐞 𝐂𝐚𝐩𝐭𝐮𝐫𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐦𝐨𝐛𝐢𝐥𝐞 𝐩𝐡𝐨𝐧𝐞
Currently not available, but in the future, it will offer a new way to create worlds in Horizon and will be the easiest way to bring physical spaces to the digital world. Creators can capture physical environments on their mobile device and invite friends, fans, or customers to visit and engage in the digital replicas.𝐂𝐥𝐨𝐮𝐝-𝐛𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠
Using Gaussian Splatting, a 3D modeling technique that renders fine details with high accuracy and efficiency, we process the model input data in the cloud and render the created model through cloud rendering and streaming on Quest 3 and the just announced Quest 3S.𝐓𝐫𝐲 𝐢𝐭 𝐨𝐮𝐭 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟
If you are in the US and you have a Meta Quest 3 or 3S you can try it out here:https://www.meta.com/experiences/meta-horizon-hyperscape-demo/7972066712871980/
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2DGS – 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
A 2D Gaussian Splats technique for extracting cleaner 3D geometry from 3DGS
https://github.com/hbb1/2d-gaussian-splatting
https://surfsplatting.github.io/
https://colab.research.google.com/drive/1qoclD7HJ3-o0O1R8cvV3PxLhoDCMsH8W
3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. However, 3DGS fails to accurately represent surfaces due to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. Our key idea is to collapse the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D Gaussians, 2D Gaussians provide view-consistent geometry while modeling surfaces intrinsically. To accurately recover thin surfaces and achieve stable optimization, we introduce a perspective-accurate 2D splatting process utilizing ray-splat intersection and rasterization. Additionally, we incorporate depth distortion and normal consistency terms to further enhance the quality of the reconstructions. We demonstrate that our differentiable renderer allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
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