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Depth pro: Sharp monocular metric depth in less than a second
We present a foundation model for zero-shot metric monocular depth estimation. Our model,
Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high …
Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high …
Patchfusion: An end-to-end tile-based framework for high-resolution monocular metric depth estimation
Single image depth estimation is a foundational task in computer vision and generative
modeling. However prevailing depth estimation models grapple with accommodating the …
modeling. However prevailing depth estimation models grapple with accommodating the …
Rgb guided tof imaging system: a survey of deep learning-based methods
Integrating an RGB camera into a ToF imaging system has become a significant technique
for perceiving the real world. The RGB guided ToF imaging system is crucial to several …
for perceiving the real world. The RGB guided ToF imaging system is crucial to several …
Sgnet: Structure guided network via gradient-frequency awareness for depth map super-resolution
Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution
(LR) one, where RGB image is often used to promote this task. Recent image guided DSR …
(LR) one, where RGB image is often used to promote this task. Recent image guided DSR …
Urcdc-depth: Uncertainty rectified cross-distillation with cutflip for monocular depth estimation
This work aims to estimate a high-quality depth map from a single RGB image. Due to the
lack of depth clues, making full use of the long-range correlation and local information is …
lack of depth clues, making full use of the long-range correlation and local information is …
Single stage adaptive multi-attention network for image restoration
Recently attention-based networks have been successful for image restoration tasks.
However, existing methods are either computationally expensive or have limited receptive …
However, existing methods are either computationally expensive or have limited receptive …
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation
This paper introduces PatchRefiner, an advanced framework for metric single image depth
estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial for …
estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial for …
Skipdiff: Adaptive skip diffusion model for high-fidelity perceptual image super-resolution
It is well-known that image quality assessment usually meets with the problem of perception-
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …
SparseDC: depth completion from sparse and non-uniform inputs
We propose SparseDC, a model for Depth Completion from Sparse and non-uniform inputs.
Unlike previous methods focusing on completing fixed distributions on benchmark datasets …
Unlike previous methods focusing on completing fixed distributions on benchmark datasets …
Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks And Its Multi-Modal Images Extension
Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a
promising way to improve the clinical diagnostic experience by significantly reducing scan …
promising way to improve the clinical diagnostic experience by significantly reducing scan …