On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks
Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep
learning, a common approach to this end is to train a neural network to estimate the …
learning, a common approach to this end is to train a neural network to estimate the …
Diffcad: Weakly-supervised probabilistic cad model retrieval and alignment from an rgb image
Perceiving 3D structures from RGB images based on CAD model primitives can enable an
effective, efficient 3D object-based representation of scenes. However, current approaches …
effective, efficient 3D object-based representation of scenes. However, current approaches …
High-resolution depth estimation for 360deg panoramas through perspective and panoramic depth images registration
CH Peng, J Zhang - Proceedings of the IEEE/CVF Winter …, 2023 - openaccess.thecvf.com
We propose a novel approach to compute high-resolution (2048x1024 and higher) depths
for panoramas that is significantly faster and qualitatively and qualitatively more accurate …
for panoramas that is significantly faster and qualitatively and qualitatively more accurate …
Monovan: Visual attention for self-supervised monocular depth estimation
Depth estimation is crucial in various computer vision applications, including autonomous
driving, robotics, and virtual and augmented reality. An accurate scene depth map is …
driving, robotics, and virtual and augmented reality. An accurate scene depth map is …
Out-of-Distribution Detection for Monocular Depth Estimation
J Hornauer, A Holzbock… - Proceedings of the …, 2023 - openaccess.thecvf.com
In monocular depth estimation, uncertainty estimation approaches mainly target the data
uncertainty introduced by image noise. In contrast to prior work, we address the uncertainty …
uncertainty introduced by image noise. In contrast to prior work, we address the uncertainty …
MobileDepth: Monocular depth estimation based on lightweight vision transformer
Y Li, X Wei - Applied Artificial Intelligence, 2024 - Taylor & Francis
As deep learning takes off, monocular depth estimation based on convolutional neural
networks (CNNs) has made impressive progress. CNNs are superior at extracting local …
networks (CNNs) has made impressive progress. CNNs are superior at extracting local …
AMENet is a monocular depth estimation network designed for automatic stereoscopic display
T Wu, Z **a, M Zhou, LB Kong, Z Chen - Scientific Reports, 2024 - nature.com
Monocular depth estimation has a wide range of applications in the field of autostereoscopic
displays, while accuracy and robustness in complex scenes are still a challenge. In this …
displays, while accuracy and robustness in complex scenes are still a challenge. In this …
Learning from the Giants: A Practical Approach to Underwater Depth and Surface Normals Estimation
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D
reconstruction, autonomous navigation, and underwater exploration. Current methods rely …
reconstruction, autonomous navigation, and underwater exploration. Current methods rely …
3D Hand Mesh Recovery from Monocular RGB in Camera Space
With the rapid advancement of technologies such as virtual reality, augmented reality, and
gesture control, users expect interactions with computer interfaces to be more natural and …
gesture control, users expect interactions with computer interfaces to be more natural and …
High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration
CH Peng, J Zhang - arxiv preprint arxiv:2210.10414, 2022 - arxiv.org
We propose a novel approach to compute high-resolution (2048x1024 and higher) depths
for panoramas that is significantly faster and qualitatively and qualitatively more accurate …
for panoramas that is significantly faster and qualitatively and qualitatively more accurate …