10 papers
αDepth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion
Xiang Zhang, Yang Zhang, Lukas Mehl +3
Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existi…
Efficient All-Pairs Correlation Volume Sampling for Optical Flow Estimation
Karlis Martins Briedis, Markus Gross, Christopher Schroers
Recent optical flow estimation methods often employ local cost sampling from a dense all-pairs correlation volume. This results in quadratic computational and memory complexity in…
UniFixer: A Universal Reference-Guided Fixer for Diffusion-Based View Synthesis
Sihan Chen, Xiang Zhang, Yang Zhang +2
With the recent surge of generative models, diffusion-based approaches have become mainstream for view synthesis tasks, either in an explicit depth-warp-inpaint or in an implicit e…
RenderFlow: Single-Step Neural Rendering via Flow Matching
Shenghao Zhang, Runtao Liu, Christopher Schroers +1
Conventional physically based rendering (PBR) pipelines generate photorealistic images through computationally intensive light transport simulations. Although recent deep learning…
Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views
Xiang Zhang, Yang Zhang, Lukas Mehl +2
Soft boundaries, like thin hairs, are commonly observed in natural and computer-generated imagery, but they remain challenging for 3D vision due to the ambiguous mixing of foregrou…
StableDPT: Temporal Stable Monocular Video Depth Estimation
Ivan Sobko, Hayko Riemenschneider, Markus Gross +1
Applying single image Monocular Depth Estimation (MDE) models to video sequences introduces significant temporal instability and flickering artifacts. We propose a novel approach t…