Detailed Geometry and Appearance from Opportunistic Motion

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• The proposed method recovers significantly more accurate geometry and appearance than state-of-the-art baselines when tested on synthetic and real-world datasets with extremely sparse viewpoints.result 95%
• The method utilizes opportunistic object motion to create effective dense viewpoints from a sparse set of static cameras.method 90%
• A novel appearance model is introduced that factorizes diffuse and specular components with reflected directional probing within the spherical harmonics space.method 90%
• The method employs an alternating minimization framework to jointly optimize 6DoF trajectories and primitive parameters for pose and geometry estimation.method 90%
• The proposed method can recover detailed surface details by capturing complex appearance variations of a moving object under static illumination.result 85%
• The method's applicability is limited by the requirement of opportunistic object motion, which may not be present in all monitored environments.limitation 70%
• The method can potentially replace the need for expensive dense camera installations in monitoring systems.market 70%
• Pose estimation and Gaussian refinement require approximately 3 and 4 minutes per frame, respectively, with a total optimization time of around 7 hours for a typical sequence.technical 90%
• The method recovers significantly more accurate geometry and appearance than state-of-the-art baselines when tested on synthetic and real-world datasets with extremely sparse viewpoints.result 95%
• The method utilizes opportunistic object motion to create effective dense viewpoints from a sparse set of static cameras.method 90%
• A joint pose and shape optimization is formulated using 2D Gaussian splatting with alternating minimization of 6DoF trajectories and primitive parameters.method 90%
• A novel appearance model is introduced that factorizes diffuse and specular components with reflected directional probing within the spherical harmonics space.method 90%

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