GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object
  with Gaussian Splatting
Reconstructing and rendering 3D objects from highly sparse views is of
critical importance for promoting applications of 3D vision techniques and
improving user experience. However, images from sparse views only contain very
limited 3D information, leading to two significant challenges: 1) Difficulty in
building multi-view consistency as images for matching are too few; 2)
Partially omitted or highly compressed object information as view coverage is
insufficient. To tackle these challenges, we propose GaussianObject, a
framework to represent and render the 3D object with Gaussian splatting, that
achieves high rendering quality with only 4 input images. We first introduce
techniques of visual hull and floater elimination which explicitly inject
structure priors into the initial optimization process for helping build
multi-view consistency, yielding a coarse 3D Gaussian representation. Then we
construct a Gaussian repair model based on diffusion models to supplement the
omitted object information, where Gaussians are further refined. We design a
self-generating strategy to obtain image pairs for training the repair model.
Our GaussianObject is evaluated on several challenging datasets, including
MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
results from only 4 views and significantly outperforming previous
state-of-the-art methods.
65d2ebe60cb9d915d37a3b19
Chen Yang
claimed_verified
2024-02-19T09:33:51.212Z
6440a1c0e44f30a72325e9ea
/avatars/b76919a4aeaedc8c735a1bcfc50136c1.svg
Chen Yang
user
chen-yang-ln
65d2ebe60cb9d915d37a3b1a
Sikuang Li
extracted_confirmed
2024-02-20T08:22:48.591Z
643cad37b409fef15e0d0819
/avatars/5dd28c5b9f5a608dfd584d6affccbdd0.svg
UranusITS
user
UranusITS
65d2ebe60cb9d915d37a3b1b
Jiemin Fang
admin_assigned
2024-02-19T11:17:03.313Z
652cf49cc6857682d3168f5a
/avatars/9ccffa04832429aad37320a301ea8e36.svg
Jiemin Fang
user
JieminFang
65d2ebe60cb9d915d37a3b1c
Ruofan Liang
65d2ebe60cb9d915d37a3b1d
Lingxi Xie
65d2ebe60cb9d915d37a3b1e
Xiaopeng Zhang
admin_assigned
2024-02-19T11:17:28.101Z
640bea9cb4f73086cddbe0a6
/avatars/ae460dd971de1d04660b266b22f81f9d.svg
Xiaopeng Zhang
user
xpzhang
65d2ebe60cb9d915d37a3b1f
Wei Shen
65d2ebe60cb9d915d37a3b20
Qi Tian
GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object
  with Gaussian Splatting
Reconstructing and rendering 3D objects from highly sparse views is of
critical importance for promoting applications of 3D vision techniques and
improving user experience. However, images from sparse views only contain very
limited 3D information, leading to two significant challenges: 1) Difficulty in
building multi-view consistency as images for matching are too few; 2)
Partially omitted or highly compressed object information as view coverage is
insufficient. To tackle these challenges, we propose GaussianObject, a
framework to represent and render the 3D object with Gaussian splatting, that
achieves high rendering quality with only 4 input images. We first introduce
techniques of visual hull and floater elimination which explicitly inject
structure priors into the initial optimization process for helping build
multi-view consistency, yielding a coarse 3D Gaussian representation. Then we
construct a Gaussian repair model based on diffusion models to supplement the
omitted object information, where Gaussians are further refined. We design a
self-generating strategy to obtain image pairs for training the repair model.
Our GaussianObject is evaluated on several challenging datasets, including
MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
results from only 4 views and significantly outperforming previous
state-of-the-art methods.
GaussianObject framework uses Gaussian splatting and diffusion models to reconstruct and render 3D objects from sparse views, achieving high-quality results with only 4 images.
Gaussian splatting diffusion models visual hull floater elimination Gaussian repair model self-generating strategy MipNeRF360 OmniObject3D OpenIllumination