正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

📢 We introduce PPISP, a physically plausible camera module for Gaussian Splatting that reduces floaters under varying appearance and predicts camera corrections for novel views such that they closely match what a real camera would output. #CV #3DGS

Zan Gojcic

3,072 subscribers

26,084 次观看 • 4 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos Contributions: • An incremental joint optimization approach for simultaneous camera pose and 3DGS reconstruction, reducing local minima and ensuring global consistency. • A robust pose estimation module leveraging learned 3D priors for accurate camera pose estimation. • An adaptive Octree Anchor Formation strategy that significantly reduces memory usage while preserving reconstruction quality.

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos Contributions: • An incremental joint optimization approach for simultaneous camera pose and 3DGS reconstruction, reducing local minima and ensuring global consistency. • A robust pose estimation module leveraging learned 3D priors for accurate camera pose estimation. • An adaptive Octree Anchor Formation strategy that significantly reduces memory usage while preserving reconstruction quality.

MrNeRF

35,969 次观看 • 9 个月前

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗨𝗩𝗚𝗦 We introduce 𝗨𝗩𝗚𝗦, a new 2D representation of 3D Gaussian Splatting (3DGS) that leverages spherical mapping. Website: Paper:

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗨𝗩𝗚𝗦 We introduce 𝗨𝗩𝗚𝗦, a new 2D representation of 3D Gaussian Splatting (3DGS) that leverages spherical mapping. Website: Paper:

Nikolaos Sarafianos

10,707 次观看 • 1 年前

"YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting" TL;DR: a unified 3D Gaussian splatting model that reconstructs high-quality scene geometry and camera poses from unposed/uncalibrated images in a single forward pass.

"YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting" TL;DR: a unified 3D Gaussian splatting model that reconstructs high-quality scene geometry and camera poses from unposed/uncalibrated images in a single forward pass.

Alexandre Morgand

14,839 次观看 • 3 个月前

Forget about #Sora. DUSt3R is the real deal. I took two pictures of our kitchen that barely overlap. It took << 2sec on a RTX 4090 to reconstruct it in an insane quality. Can we get out a point cloud for Gaussian Splatting #3DGS training + the camera poses?

Forget about #Sora. DUSt3R is the real deal. I took two pictures of our kitchen that barely overlap. It took << 2sec on a RTX 4090 to reconstruct it in an insane quality. Can we get out a point cloud for Gaussian Splatting #3DGS training + the camera poses?

MrNeRF

103,255 次观看 • 2 年前

GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views TL;DR: Are we witnessing the first steps towards 3DGS live streaming? Contributions: • We introduce a generalizable 3D Gaussian Splatting methodology that employs pixel-wise Gaussian parameter maps defined on 2D source image planes to formulate 3D Gaussians in a feed-forward manner. • We propose a fully differentiable framework composed of an iterative depth estimation module and a Gaussian parameter regression module. The intermediate depth prediction bridges the two components and allows them to benefit from joint training. • We introduce a regularization term and an epipolar attention mechanism to preserve geometry consistency between the two source views when using only rendering loss. Our method generalizes well to unseen characters even in complicated scenes. • We develop a real-time FVV system that achieves high-resolution rendering of characters in the scene without any geometry supervision.

GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views TL;DR: Are we witnessing the first steps towards 3DGS live streaming? Contributions: • We introduce a generalizable 3D Gaussian Splatting methodology that employs pixel-wise Gaussian parameter maps defined on 2D source image planes to formulate 3D Gaussians in a feed-forward manner. • We propose a fully differentiable framework composed of an iterative depth estimation module and a Gaussian parameter regression module. The intermediate depth prediction bridges the two components and allows them to benefit from joint training. • We introduce a regularization term and an epipolar attention mechanism to preserve geometry consistency between the two source views when using only rendering loss. Our method generalizes well to unseen characters even in complicated scenes. • We develop a real-time FVV system that achieves high-resolution rendering of characters in the scene without any geometry supervision.

MrNeRF

25,699 次观看 • 1 年前

Testing my DJI Osmo 360 rig for gaussian splatting. Mason’s Avenue, London. 14 million splats total. Trained with LichtFeld Studio and gsplat, edited in Houdini GSOPs, camera animation in Unity and rendered with Deckard Render. #gaussiansplatting #3DGS

Testing my DJI Osmo 360 rig for gaussian splatting. Mason’s Avenue, London. 14 million splats total. Trained with LichtFeld Studio and gsplat, edited in Houdini GSOPs, camera animation in Unity and rendered with Deckard Render. #gaussiansplatting #3DGS

grade eterna

187,977 次观看 • 2 个月前

RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting Contributions: • We introduce a unified surface-volume Gaussian scene representation for jointly modeling sharp specular reflections and clear transmission in real-world scenes containing thin semi-transparent surfaces. • We propose Specular-Aware Gradient Gating to suppress misleading gradients from complex specular regions, substantially reducing floaters in the transmission branch. • Extensive experiments demonstrate that RT-Splatting significantly outperforms prior methods while maintaining real-time rendering and enabling flexible scene editing.

RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting Contributions: • We introduce a unified surface-volume Gaussian scene representation for jointly modeling sharp specular reflections and clear transmission in real-world scenes containing thin semi-transparent surfaces. • We propose Specular-Aware Gradient Gating to suppress misleading gradients from complex specular regions, substantially reducing floaters in the transmission branch. • Extensive experiments demonstrate that RT-Splatting significantly outperforms prior methods while maintaining real-time rendering and enabling flexible scene editing.

MrNeRF

27,917 次观看 • 26 天前

Happy to introduce Habitat-GS, a non-intrusive extension of Habitat-Sim that brings dynamic Gaussian Splatting for photorealistic rendering and comes with hundreds of high-quality 3DGS scene assets, aiming to empowering navigation research. Code:

Happy to introduce Habitat-GS, a non-intrusive extension of Habitat-Sim that brings dynamic Gaussian Splatting for photorealistic rendering and comes with hundreds of high-quality 3DGS scene assets, aiming to empowering navigation research. Code:

Sida Peng

11,982 次观看 • 1 个月前

Radiance field reconstruction (Gaussian splatting) quality is getting a big step up. NVIDIA AI just released Physically-Plausible Image Signal Processing (PPISP) for Radiance Field Reconstruction. Apache 2.0 and coming to both gsplat and 3DGRUT. Code: Article: Authors: Isaac Deutsch, Nicolas Möenne-Loccoz, Zan Gojcic, Gavriel State NVIDIA AIDev

Radiance field reconstruction (Gaussian splatting) quality is getting a big step up. NVIDIA AI just released Physically-Plausible Image Signal Processing (PPISP) for Radiance Field Reconstruction. Apache 2.0 and coming to both gsplat and 3DGRUT. Code: Article: Authors: Isaac Deutsch, Nicolas Möenne-Loccoz, Zan Gojcic, Gavriel State NVIDIA AIDev

Radiance Fields

49,455 次观看 • 4 个月前

📢BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading We propose a hybrid neural shading scheme for creating intrinsically decomposed 3DGS head avatars, that allow real-time relighting and animation. 🌍 📷

📢BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading We propose a hybrid neural shading scheme for creating intrinsically decomposed 3DGS head avatars, that allow real-time relighting and animation. 🌍 📷

Matthias Niessner

23,001 次观看 • 1 年前

I'm testing tree generation using only two reference images. Basically, my Gaussian Splatting generation technique works with almost anything. I can't wait to see the final result once I’ve gathered a huge library of realistic assets for my game. For optimization, I’m planning to use a LOD system that dynamically reduces the number of Gaussian splats based on the distance from the camera.

I'm testing tree generation using only two reference images. Basically, my Gaussian Splatting generation technique works with almost anything. I can't wait to see the final result once I’ve gathered a huge library of realistic assets for my game. For optimization, I’m planning to use a LOD system that dynamically reduces the number of Gaussian splats based on the distance from the camera.

Alex

30,458 次观看 • 27 天前

NOTA says MacG represents what we can’t say on camera: “I agree with that, I would say that—but not on camera,” 🤔

NOTA says MacG represents what we can’t say on camera: “I agree with that, I would say that—but not on camera,” 🤔

YaseBlock B 🇸🇿

23,773 次观看 • 1 年前

Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.

Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.

MrNeRF

27,277 次观看 • 1 年前

Before about 2010, if someone pulled out a video camera it was so novel that people would get super excited. They felt like they were on television

Before about 2010, if someone pulled out a video camera it was so novel that people would get super excited. They felt like they were on television

TastefulLindy

37,314 次观看 • 3 个月前

HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting Contributions: First, we propose Homogeneous Gaussian Splatting (HoGS), a novel method adopting homogeneous coordinates to represent positions and scales of 3DGS for realistic and real-time rendering of both near and far objects. Second, despite the ultimate simplicity of HoGS, our method achieves state-of-the-art NVS results compared to other implicit and explicit representations.

HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting Contributions: First, we propose Homogeneous Gaussian Splatting (HoGS), a novel method adopting homogeneous coordinates to represent positions and scales of 3DGS for realistic and real-time rendering of both near and far objects. Second, despite the ultimate simplicity of HoGS, our method achieves state-of-the-art NVS results compared to other implicit and explicit representations.

MrNeRF

22,978 次观看 • 1 年前

3D Gaussian Splatting is great, but can it work without the pre-computed camera poses? Introducing: COLMAP-Free 3D Gaussian Splatting Our recent work shows not only it can, but 3D Gaussians make camera pose estimation easy (compared to NeRF) along with reconstruction. 👇🧵

3D Gaussian Splatting is great, but can it work without the pre-computed camera poses? Introducing: COLMAP-Free 3D Gaussian Splatting Our recent work shows not only it can, but 3D Gaussians make camera pose estimation easy (compared to NeRF) along with reconstruction. 👇🧵

Xiaolong Wang

76,747 次观看 • 2 年前

WE GOT THE CAMERA ANGLE FROM WHAT THEY WERE FILMING ON STAGE FOR A SEC

WE GOT THE CAMERA ANGLE FROM WHAT THEY WERE FILMING ON STAGE FOR A SEC

𝐤𝐫𝐢𝐬𝐭𝐞𝐧⸆⸉ ❤️‍🔥

1,366,391 次观看 • 1 年前

[SIGGRAPH 2025] Photoreal Scene Reconstruction from an Egocentric Device Contributions: 1. We address the importance of employing visual-inertial bundle adjustment (VIBA) that accounts for the rolling-shutter behavior of the RGB camera. This provides a continuous camera trajectory to model pixel movement in neural reconstruction. Our experiments demonstrate that using VIBA consistently improves the novel view quality in Gaussian Splatting by +1 dB in PSNR. 2. We introduce a rasterization-based image formulation pipeline that addresses common artifacts in physical image formation, including rolling shutter, lens shading, exposure, and gain compensation. Our approach is distinct in that we represent image poses as posed pixel arrays sampled from a continuous trajectory, rather than assigning a single camera pose per image, and preserve the merit of Gaussian rasterization. Unlike existing methods that require ray-tracing Gaussians, e.g., [Moenne-Loccoz et al. 2024], our formulation is applicable to general-purpose rasterization-based Gaussian splatting. When applied to 3D Gaussian Splatting (3DGS) [Kerbl et al. 2023], our approach can further enhance reconstruction quality by +1 dB. We outperform existing baselines and demonstrate a substantial quality improvement in handling complex scenes observed by egocentric devices. 3. To reduce the effect of blur from rapid head motion in darker indoor scenes, we propose a strategy of deliberately underexposing input videos during capture, inspired by HDR+ [Hasinoff et al. 2016]. We demonstrate that we can reconstruct high-quality, noise-free scene radiance from noisy, dim input videos, and further render sharp, blur-free videos at a higher dynamic range.

[SIGGRAPH 2025] Photoreal Scene Reconstruction from an Egocentric Device Contributions: 1. We address the importance of employing visual-inertial bundle adjustment (VIBA) that accounts for the rolling-shutter behavior of the RGB camera. This provides a continuous camera trajectory to model pixel movement in neural reconstruction. Our experiments demonstrate that using VIBA consistently improves the novel view quality in Gaussian Splatting by +1 dB in PSNR. 2. We introduce a rasterization-based image formulation pipeline that addresses common artifacts in physical image formation, including rolling shutter, lens shading, exposure, and gain compensation. Our approach is distinct in that we represent image poses as posed pixel arrays sampled from a continuous trajectory, rather than assigning a single camera pose per image, and preserve the merit of Gaussian rasterization. Unlike existing methods that require ray-tracing Gaussians, e.g., [Moenne-Loccoz et al. 2024], our formulation is applicable to general-purpose rasterization-based Gaussian splatting. When applied to 3D Gaussian Splatting (3DGS) [Kerbl et al. 2023], our approach can further enhance reconstruction quality by +1 dB. We outperform existing baselines and demonstrate a substantial quality improvement in handling complex scenes observed by egocentric devices. 3. To reduce the effect of blur from rapid head motion in darker indoor scenes, we propose a strategy of deliberately underexposing input videos during capture, inspired by HDR+ [Hasinoff et al. 2016]. We demonstrate that we can reconstruct high-quality, noise-free scene radiance from noisy, dim input videos, and further render sharp, blur-free videos at a higher dynamic range.

MrNeRF

15,244 次观看 • 1 年前