arXiv:2307.15988
공식 코드 · Papers with Code
RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects
원문 초록
We present RGB-D-Fusion, a multi-modal conditional denoising diffusion probabilistic model to generate high resolution depth maps from low-resolution monocular RGB images of humanoid subjects. RGB-D-Fusion first generates a low-resolution depth map using an image conditioned denoising diffusion probabilistic model and then upsamples the depth map using a second denoising diffusion probabilistic model conditioned on a low-resolution RGB-D image. We further introduce a novel augmentation technique, depth noise augmentation, to increase the robustness of our super-resolution model.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
Grids Often Outperform Implicit Neural Representations
Exploring Diffusion with Test-Time Training on Efficient Image Restoration
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI
토론
이 논문과 연결된 포럼 글이 아직 없습니다.