From 35ad01585d1df5975a75b73eacb98f88a1bafdd1 Mon Sep 17 00:00:00 2001 From: Janusch Patas Date: Sun, 12 Jan 2025 22:06:41 +0100 Subject: [PATCH] update papers --- assets/thumbnails/chahe2024query3d.jpg | Bin 0 -> 31321 bytes assets/thumbnails/chen2024dogs.jpg | Bin 0 -> 24641 bytes assets/thumbnails/fan2024momentumgs.jpg | Bin 0 -> 29509 bytes assets/thumbnails/hanson2024pup.jpg | Bin 0 -> 29278 bytes assets/thumbnails/huang2024endo4dgs.jpg | Bin 0 -> 18282 bytes assets/thumbnails/huang2025fatesgs.jpg | Bin 0 -> 33037 bytes assets/thumbnails/kaleta2024lumigauss.jpg | Bin 0 -> 31574 bytes assets/thumbnails/peng2024gags.jpg | Bin 0 -> 30475 bytes assets/thumbnails/stearns2024dynamic.jpg | Bin 0 -> 31049 bytes assets/thumbnails/sun2024splatter.jpg | Bin 0 -> 25297 bytes assets/thumbnails/wang2024contextgs.jpg | Bin 0 -> 25880 bytes assets/thumbnails/wang2024vidu4d.jpg | Bin 0 -> 24826 bytes assets/thumbnails/wu2024deblur4dgs.jpg | Bin 0 -> 29277 bytes assets/thumbnails/yan20244d.jpg | 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z`{uLR2VX)b!7>jv1Q>X;lb&UnCF%}2A8C=+@2P4_A7Rt;^{O-y#AC$_sH5L<()tZ; z{xOyb#D*orDp{}K;F?JT2$jICVZUE|asCSX^#ZCI*jBX@Rbd&JZ{|v#l$aJKN#nU1 zIgu>+ha~*4+gbg~;5oyw2A>z#)yIhT5ucQ+v+L06DkEdVPW&>ERmzfVk2p&4lqZ^H z#P6-+?Y9>F@luhh~Pn_BjC@tINl=#XNIp1mbC@8DNzg6N3s*{a{^72GGznr}_PotgLb zU%*Cu6M1Iet9Cj66#grW0Ap0Qe{xk7lL~+uK&IART=vF)kHJc6J2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors' @@ -4116,6 +4623,40 @@ - Sparse thumbnail: assets/thumbnails/paul2024spsup2sup360.jpg publication_date: '2024-05-26T11:01:39+00:00' +- id: chen2024dogs + title: 'DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction + Via Gaussian Consensus' + authors: Yu Chen, Gim Hee Lee + year: '2024' + abstract: 'The recent advances in 3D Gaussian Splatting (3DGS) show promising results + on the novel view synthesis (NVS) task. With its superior rendering performance + and high-fidelity rendering quality, 3DGS is excelling at its previous NeRF counterparts. + The most recent 3DGS method focuses either on improving the instability of rendering + efficiency or reducing the model size. On the other hand, the training efficiency + of 3DGS on large-scale scenes has not gained much attention. In this work, we + propose DoGaussian, a method that trains 3DGS distributedly. Our method first + decomposes a scene into K blocks and then introduces the Alternating Direction + Method of Multipliers (ADMM) into the training procedure of 3DGS. During training, + our DOGS maintains one global 3DGS model on the master node and K local 3DGS models + on the slave nodes. The K local 3DGS models are dropped after training and we + only query the global 3DGS model during inference. The training time is reduced + by scene decomposition, and the training convergence and stability are guaranteed + through the consensus on the shared 3D Gaussians. Our method accelerates the training + of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving + state-of-the-art rendering quality. Our code is publicly available at https://github.com/AIBluefisher/DOGS. + + ' + project_page: https://aibluefisher.github.io/DOGS/ + paper: https://arxiv.org/pdf/2405.13943.pdf + code: https://github.com/AIBluefisher/DOGS + video: null + tags: + - Code + - Large-Scale + - Project + thumbnail: assets/thumbnails/chen2024dogs.jpg + publication_date: '2024-05-22T19:17:58+00:00' + date_source: arxiv - id: dalal2024gaussian title: 'Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review' authors: Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr, Kristian Muri Knausgård @@ -8780,6 +9321,39 @@ - Virtual Reality thumbnail: assets/thumbnails/jiang2024vrgs.jpg publication_date: '2024-01-30T01:28:36+00:00' +- id: huang2024endo4dgs + title: 'Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting' + authors: Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam, + Hongliang Ren + year: '2024' + abstract: 'In the realm of robot-assisted minimally invasive surgery, dynamic scene + reconstruction can significantly enhance downstream tasks and improve surgical + outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence + for their exceptional ability to reconstruct scenes but are hampered by slow inference + speed, prolonged training, and inconsistent depth estimation. Some previous work + utilizes ground truth depth for optimization but is hard to acquire in the surgical + domain. To overcome these obstacles, we present Endo-4DGS, a real-time endoscopic + dynamic reconstruction approach that utilizes 3D Gaussian Splatting (GS) for 3D + representation. Specifically, we propose lightweight MLPs to capture temporal + dynamics with Gaussian deformation fields. To obtain a satisfactory Gaussian Initialization, + we exploit a powerful depth estimation foundation model, Depth-Anything, to generate + pseudo-depth maps as a geometry prior. We additionally propose confidence-guided + learning to tackle the ill-pose problems in monocular depth estimation and enhance + the depth-guided reconstruction with surface normal constraints and depth regularization. + Our approach has been validated on two surgical datasets, where it can effectively + render in real-time, compute efficiently, and reconstruct with remarkable accuracy. + + ' + project_page: null + paper: https://arxiv.org/pdf/2401.16416.pdf + code: null + video: null + tags: + - Dynamic + - Medicine + thumbnail: assets/thumbnails/huang2024endo4dgs.jpg + publication_date: '2024-01-29T18:55:29+00:00' + date_source: arxiv - id: feng2024gaussian title: 'Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting' authors: Yutao Feng, Xiang Feng, Yintong Shang, Ying Jiang, Chang Yu, Zeshun Zong, @@ -10728,7 +11302,7 @@ thumbnail: assets/thumbnails/xiang2023flashavatar.jpg publication_date: '2023-12-03T07:23:53+00:00' - id: das2023neural - title: null + title: Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction authors: Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen year: '2023' abstract: 'Reconstructing dynamic objects from monocular videos is a severely underconstrained