A geometry-aware, training-free inference framework that refines pretrained video diffusion predictions with projected static history content and view-conditioned routing achieves fifth place on AI City Challenge Track 5.
3D Photography using Context-aware Layered Depth Inpainting
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show fewer artifacts compared with the state of the arts.
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction
A geometry-aware, training-free inference framework that refines pretrained video diffusion predictions with projected static history content and view-conditioned routing achieves fifth place on AI City Challenge Track 5.