Presentation
Generative 3D Gaussians with Learned Density Control
SessionGenerative 3D (2)
DescriptionDeG is an image-to-3D generative framework for Gaussian splatting that learns to place detail adaptively. It evaluates each Gaussian’s effect on rendering loss, then reinforces density in important regions and suppresses less useful ones, enabling variable-resolution, high-quality 3D asset generation.
Event Type
Technical Paper
TimeTuesday, 21 July 20269:50am - 10:00am PDT
LocationRoom 403 B
Digital Library
PDF
Session Time & Location
Sunday, 19 July 20266:00pm - 8:45pm PDTHall K
Tuesday, 21 July 20269:00am - 10:30am PDTRoom 403 B
Artificial Intelligence/Machine Learning
Modeling
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