SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D
  Gaussians from Monocular Video
Synthesizing novel views from in-the-wild monocular videos is challenging due
to scene dynamics and the lack of multi-view cues. To address this, we propose
SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for
high-quality reconstruction and fast rendering from monocular videos. At its
core is a novel Motion-Adaptive Spline (MAS) method, which represents
continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a
small number of control points. For MAS, we introduce a Motion-Adaptive Control
points Pruning (MACP) method to model the deformation of each dynamic 3D
Gaussian across varying motions, progressively pruning control points while
maintaining dynamic modeling integrity. Additionally, we present a joint
optimization strategy for camera parameter estimation and 3D Gaussian
attributes, leveraging photometric and geometric consistency. This eliminates
the need for Structure-from-Motion preprocessing and enhances SplineGS's
robustness in real-world conditions. Experiments show that SplineGS
significantly outperforms state-of-the-art methods in novel view synthesis
quality for dynamic scenes from monocular videos, achieving thousands times
faster rendering speed.
67622196ebfe62ccbf72e469
Jongmin Park
67622196ebfe62ccbf72e46a
Minh-Quan Viet Bui
67622196ebfe62ccbf72e46b
Juan Luis Gonzalez Bello
67622196ebfe62ccbf72e46c
Jaeho Moon
67622196ebfe62ccbf72e46d
Jihyong Oh
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2024-12-18T12:38:34.804Z
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/avatars/b533e776aa3d95d722b46ef0cd381acd.svg
Jihyong Oh
user
ozbro
67622196ebfe62ccbf72e46e
Munchurl Kim
SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D
  Gaussians from Monocular Video
Synthesizing novel views from in-the-wild monocular videos is challenging due
to scene dynamics and the lack of multi-view cues. To address this, we propose
SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for
high-quality reconstruction and fast rendering from monocular videos. At its
core is a novel Motion-Adaptive Spline (MAS) method, which represents
continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a
small number of control points. For MAS, we introduce a Motion-Adaptive Control
points Pruning (MACP) method to model the deformation of each dynamic 3D
Gaussian across varying motions, progressively pruning control points while
maintaining dynamic modeling integrity. Additionally, we present a joint
optimization strategy for camera parameter estimation and 3D Gaussian
attributes, leveraging photometric and geometric consistency. This eliminates
the need for Structure-from-Motion preprocessing and enhances SplineGS's
robustness in real-world conditions. Experiments show that SplineGS
significantly outperforms state-of-the-art methods in novel view synthesis
quality for dynamic scenes from monocular videos, achieving thousands times
faster rendering speed.
SplineGS, a COLMAP-free 3D Gaussian Splatting framework, uses Motion-Adaptive Splines and joint optimization to deliver high-quality dynamic scene reconstructions and fast rendering from monocular videos.
3D Gaussian Splatting Motion-Adaptive Spline MAS Motion-Adaptive Control points Pruning MACP joint optimization Structure-from-Motion novel view synthesis rendering speed