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FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction
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FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction
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This paper addresses the challenge of reconstructing dynamic 3D scenes with complex motions. Some recent works define 3D Gaussian primitives in the canonical space and use deformation fields to map canonical primitives to observation spaces, achieving real-time dynamic view synthesis. However, these methods often struggle to handle scenes with complex motions due to the difficulty of optimizing deformation fields. To overcome this problem, we propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations. In contrast to canonical Gaussian primitives, our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes. In addition, we endow each Gaussian primitive with an motion function, allowing it to move to neighboring regions over time, which reduces the temporal redundancy. Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin. Project page: https://zju3dv.github.io/freetimegs/ .
Forward citations
Cited by 3 Pith papers
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FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles
FreeTimeGS++ improves 4D Gaussian Splatting by using gated marginalization and neural velocity fields to achieve more stable dynamic scene representations with lower run-to-run variance.
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FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles
Analysis of 4DGS reveals temporal partitioning from Gaussian durations and a photometric-spatiotemporal discrepancy, leading to FreeTimeGS++ with gated marginalization and neural velocity fields for superior stability...
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FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles
FreeTimeGS++ improves dynamic scene reconstruction by identifying emergent temporal partitioning and photometric-motion decoupling in 4DGS, then applying targeted techniques for reduced run-to-run variance.
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