2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
  for High-Fidelity Indoor Scene Reconstruction
The reconstruction of indoor scenes remains challenging due to the inherent
complexity of spatial structures and the prevalence of textureless regions.
Recent advancements in 3D Gaussian Splatting have improved novel view synthesis
with accelerated processing but have yet to deliver comparable performance in
surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method
leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction.
Specifically, we employ a seed-guided mechanism to control the distribution of
2D Gaussians, with the density of seed points dynamically optimized through
adaptive growth and pruning mechanisms. To further improve geometric accuracy,
we incorporate monocular depth and normal priors to provide constraints for
details and textureless regions respectively. Additionally, multi-view
consistency constraints are employed to mitigate artifacts and further enhance
reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets
demonstrate that our method achieves state-of-the-art performance in indoor
scene reconstruction.
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Wanting Zhang
claimed_verified
2024-12-06T15:55:29.889Z
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Wanting Zhang
user
Valentina-Zhang
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Haodong Xiang
admin_assigned
2024-12-09T16:01:09.273Z
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Haodong Xiang
user
XHD0612
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Zhichao Liao
claimed_verified
2024-12-09T11:07:01.650Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/66eb9d5d7f17088991ecbae2/khI2KZ4ZGJ891iOf-O0BF.jpeg
Zhichao Liao
user
ChaosLiao
6752e5756d713742ea3cfd52
Xiansong Lai
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Xinghui Li
claimed_verified
2024-12-06T15:55:33.428Z
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Xinghui Li
user
Crayon-Shinchan
6752e5756d713742ea3cfd54
Long Zeng
2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
  for High-Fidelity Indoor Scene Reconstruction
The reconstruction of indoor scenes remains challenging due to the inherent
complexity of spatial structures and the prevalence of textureless regions.
Recent advancements in 3D Gaussian Splatting have improved novel view synthesis
with accelerated processing but have yet to deliver comparable performance in
surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method
leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction.
Specifically, we employ a seed-guided mechanism to control the distribution of
2D Gaussians, with the density of seed points dynamically optimized through
adaptive growth and pruning mechanisms. To further improve geometric accuracy,
we incorporate monocular depth and normal priors to provide constraints for
details and textureless regions respectively. Additionally, multi-view
consistency constraints are employed to mitigate artifacts and further enhance
reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets
demonstrate that our method achieves state-of-the-art performance in indoor
scene reconstruction.
A method using 2D Gaussian Splatting combined with seed-guided mechanisms and monocular depth/normal priors achieves state-of-the-art indoor scene reconstruction.
3D Gaussian Splatting 2D Gaussian Splatting seed-guided mechanism adaptive growth pruning mechanisms monocular depth normal priors multi-view consistency constraints ScanNet ScanNet++