Generative Densification: Learning to Densify Gaussians for
  High-Fidelity Generalizable 3D Reconstruction
Generalized feed-forward Gaussian models have achieved significant progress
in sparse-view 3D reconstruction by leveraging prior knowledge from large
multi-view datasets. However, these models often struggle to represent
high-frequency details due to the limited number of Gaussians. While the
densification strategy used in per-scene 3D Gaussian splatting (3D-GS)
optimization can be adapted to the feed-forward models, it may not be ideally
suited for generalized scenarios. In this paper, we propose Generative
Densification, an efficient and generalizable method to densify Gaussians
generated by feed-forward models. Unlike the 3D-GS densification strategy,
which iteratively splits and clones raw Gaussian parameters, our method
up-samples feature representations from the feed-forward models and generates
their corresponding fine Gaussians in a single forward pass, leveraging the
embedded prior knowledge for enhanced generalization. Experimental results on
both object-level and scene-level reconstruction tasks demonstrate that our
method outperforms state-of-the-art approaches with comparable or smaller model
sizes, achieving notable improvements in representing fine details.
675932605456a65fb50aeed5
Seungtae Nam
claimed_verified
2024-12-12T08:22:38.514Z
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Seungtae
user
stnamjef
675932605456a65fb50aeed6
Xiangyu Sun
admin_assigned
2024-12-12T09:01:38.502Z
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Sun Xiang yu
user
xysun
675932605456a65fb50aeed7
Gyeongjin Kang
claimed_verified
2024-12-11T09:19:29.174Z
64aa93e799639cc312795ea8
/avatars/904453c39c09aa51d3f6aa9fafb4a47d.svg
Gyeongjin Kang
user
Gynjn
675932605456a65fb50aeed8
Younggeun Lee
admin_assigned
2024-12-12T09:01:44.417Z
66a4a1a7d8e85b03deddfa59
/avatars/56dbec2101717ad9471e08a03ae51f0c.svg
Young geun Lee
user
LeeYG
675932605456a65fb50aeed9
Seungjun Oh
claimed_verified
2024-12-19T18:04:58.055Z
672b66744efad666d2efb0c8
/avatars/9c00a67e9d5b74694759849cca32b015.svg
Oh Seungjun
user
ohseungjun
675932605456a65fb50aeeda
Eunbyung Park
admin_assigned
2024-12-12T09:01:55.354Z
655e0141d36a195f663ee4b0
/avatars/97bb695ccefdcb2139b94bcae808cf99.svg
Eunbyung Park
user
epark
Generative Densification: Learning to Densify Gaussians for
  High-Fidelity Generalizable 3D Reconstruction
Generalized feed-forward Gaussian models have achieved significant progress
in sparse-view 3D reconstruction by leveraging prior knowledge from large
multi-view datasets. However, these models often struggle to represent
high-frequency details due to the limited number of Gaussians. While the
densification strategy used in per-scene 3D Gaussian splatting (3D-GS)
optimization can be adapted to the feed-forward models, it may not be ideally
suited for generalized scenarios. In this paper, we propose Generative
Densification, an efficient and generalizable method to densify Gaussians
generated by feed-forward models. Unlike the 3D-GS densification strategy,
which iteratively splits and clones raw Gaussian parameters, our method
up-samples feature representations from the feed-forward models and generates
their corresponding fine Gaussians in a single forward pass, leveraging the
embedded prior knowledge for enhanced generalization. Experimental results on
both object-level and scene-level reconstruction tasks demonstrate that our
method outperforms state-of-the-art approaches with comparable or smaller model
sizes, achieving notable improvements in representing fine details.
Generative Densification enhances feed-forward Gaussian models by efficiently upsampling feature representations to improve high-frequency detail representation in 3D reconstruction tasks.
Gaussian models sparse-view 3D reconstruction multi-view datasets high-frequency details 3D Gaussian splatting generative densification feature representations