3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting
DescriptionIn this paper, we present an implicit surface reconstruction method with
3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D
reconstruction with intricate details while inheriting the high efficiency and
rendering quality of 3DGS. The key insight is to incorporate an implicit
signed distance field (SDF) within 3D Gaussians for surface modeling and to
enable the alignment and joint optimization of both SDF and 3D Gaussians.
To achieve this, we design coupling strategies that align and associate the
SDF with 3D Gaussians, allowing for unified optimization and enforcing
surface constraints on the 3D Gaussians. With alignment, optimizing the 3D
Gaussians provides supervisory signals for SDF learning, enabling the recon-
struction of intricate details. However, this only offers sparse supervisory
signals to the SDF at locations occupied by Gaussians, which is insufficient
for learning a continuous SDF. Then, to address this limitation, we incor-
porate volumetric rendering and align the rendered geometric attributes
(depth, normal) with that derived from 3DGS. In sum, these two designs
allow SDF and 3DGS to be aligned, jointly optimized, and mutually boosted.
Our extensive experimental results demonstrate that our 3DGSR enables
high-quality 3D surface reconstruction while preserving the efficiency and
rendering quality of 3DGS. Besides, our method competes favorably with
leading surface reconstruction techniques while offering a more efficient
learning process and much better rendering qualities.
3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D
reconstruction with intricate details while inheriting the high efficiency and
rendering quality of 3DGS. The key insight is to incorporate an implicit
signed distance field (SDF) within 3D Gaussians for surface modeling and to
enable the alignment and joint optimization of both SDF and 3D Gaussians.
To achieve this, we design coupling strategies that align and associate the
SDF with 3D Gaussians, allowing for unified optimization and enforcing
surface constraints on the 3D Gaussians. With alignment, optimizing the 3D
Gaussians provides supervisory signals for SDF learning, enabling the recon-
struction of intricate details. However, this only offers sparse supervisory
signals to the SDF at locations occupied by Gaussians, which is insufficient
for learning a continuous SDF. Then, to address this limitation, we incor-
porate volumetric rendering and align the rendered geometric attributes
(depth, normal) with that derived from 3DGS. In sum, these two designs
allow SDF and 3DGS to be aligned, jointly optimized, and mutually boosted.
Our extensive experimental results demonstrate that our 3DGSR enables
high-quality 3D surface reconstruction while preserving the efficiency and
rendering quality of 3DGS. Besides, our method competes favorably with
leading surface reconstruction techniques while offering a more efficient
learning process and much better rendering qualities.
Event Type
Technical Papers
TimeTuesday, 3 December 20249:00am - 12:00pm JST
LocationHall C, C Block, Level 4
