From 4559c4974c9227c2a7ef24c9d2de37d8675a78cf Mon Sep 17 00:00:00 2001 From: Zhang Jiawei <66359549+jiaw-z@users.noreply.github.com> Date: Mon, 20 May 2024 15:58:44 +0800 Subject: [PATCH] Add files via upload --- index.html | 289 +++++++++++++++++++++++++++++ static/css/bulma-carousel.min.css | 1 + static/css/bulma-slider.min.css | 1 + static/css/bulma.css.map.txt | 1 + static/css/bulma.min.css | 1 + static/css/fontawesome.all.min.css | 5 + static/css/index.css | 157 ++++++++++++++++ static/images/intro_overview.png | Bin 0 -> 1377227 bytes 8 files changed, 455 insertions(+) create mode 100644 index.html create mode 100644 static/css/bulma-carousel.min.css create mode 100644 static/css/bulma-slider.min.css create mode 100644 static/css/bulma.css.map.txt create mode 100644 static/css/bulma.min.css create mode 100644 static/css/fontawesome.all.min.css create mode 100644 static/css/index.css create mode 100644 static/images/intro_overview.png diff --git a/index.html b/index.html new file mode 100644 index 0000000..fa6edbc --- /dev/null +++ b/index.html @@ -0,0 +1,289 @@ + + + + + + + + CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization + + + + + + + + + + + + + + + + + + + + + + + +
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CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization

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+ + + Jiawei Zhang1, + + + + Jiahe Li1, + + + + Xiaohan Yu3, + + + + Huang Lei2, + + + + Lin Gu4,5, + + + + Jin Zheng1, + + + + Xiao Bai1,*, + +
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+ 1Beihang University, + 2SKLCCSE, Institute of Artificial Intelligence, Beihang University, + 3School of Computing, Macquarie University, +
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+ 4RIKEN AIP, + 5The University of Tokyo +
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+ + + + + + + Paper + + + + + + + + arXiv + + + + + + + + + Video + + + + + + + + + Code (TBD) + + + + +
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+ Illustration of how the different behaviors between two 3D Gaussian radiance fields correlated to construction quality. Gaussians with different behaviors tend to not fit the ground-truth shape well. Therefore, inaccurate reconstructions can be identified by measuring the differences without accessing ground-truth information. +

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Abstract

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+ 3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting the reconstruction quality.

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+ This paper introduces a new co-regularization perspective for improving sparse-view 3DGS. When training two 3D Gaussian radiance fields with the same sparse views of a scene, we observe that the two radiance fields exhibit \textit{point disagreement} and \textit{rendering disagreement} that can unsupervisedly predict reconstruction quality, stemming from the sampling implementation in densification. We further quantify the point disagreement and rendering disagreement by evaluating the registration between Gaussians' point representations and calculating differences in their rendered pixels. The empirical study demonstrates the negative correlation between the two disagreements and accurate reconstruction, which allows us to identify inaccurate reconstruction without accessing ground-truth information. +

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+ Based on the study, we propose CoR-GS, which identifies and suppresses inaccurate reconstruction based on the two disagreements: (\romannumeral1) Co-pruning considers Gaussians that exhibit high point disagreement in inaccurate positions and prunes them. (\romannumeral2) Pseudo-view co-regularization considers pixels that exhibit high rendering disagreement are inaccurately rendered and suppress the disagreement. +

+ Results on LLFF, Mip-NeRF360, DTU, and Blender demonstrate that CoR-GS effectively regularizes the scene geometry, reconstructs the compact representations, and achieves state-of-the-art novel view synthesis quality under sparse training views. +

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Method

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+ CoR-GS trains two 3D Gaussian radiance fields with the same views and conducts co-regularization during training. It improves sparse-view 3DGS by identifying and suppressing inaccurate reconstruction based on the point disagreement and rendering disagreement. +

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Comparison

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+ Comarison with current SOTA baselines. Zoom in for better visualization. +

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