[HTML][HTML] Deep learning in optical metrology: a review

C Zuo, J Qian, S Feng, W Yin, Y Li, P Fan… - Light: Science & …, 2022 - nature.com
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need?

C Zhang, C Zhang, S Zheng, Y Qiao, C Li… - arxiv preprint arxiv …, 2023 - arxiv.org
As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

Neuralangelo: High-fidelity neural surface reconstruction

Z Li, T Müller, A Evans, RH Taylor… - Proceedings of the …, 2023 - openaccess.thecvf.com
Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …

Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction

Z Yu, S Peng, M Niemeyer, T Sattler… - Advances in neural …, 2022 - proceedings.neurips.cc
In recent years, neural implicit surface reconstruction methods have become popular for
multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these …

Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction

M Oechsle, S Peng, A Geiger - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing
surfaces from multi-view images and synthesizing novel views. Unfortunately, existing …

Adabins: Depth estimation using adaptive bins

SF Bhat, I Alhashim, P Wonka - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
We address the problem of estimating a high quality dense depth map from a single RGB
input image. We start out with a baseline encoder-decoder convolutional neural network …

Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors

G Qian, J Mai, A Hamdi, J Ren, A Siarohin, B Li… - arxiv preprint arxiv …, 2023 - arxiv.org
We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D
meshes generation from a single unposed image in the wild using both2D and 3D priors. In …

Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision

M Niemeyer, L Mescheder… - Proceedings of the …, 2020 - openaccess.thecvf.com
Learning-based 3D reconstruction methods have shown impressive results. However, most
methods require 3D supervision which is often hard to obtain for real-world datasets …

Local light field fusion: Practical view synthesis with prescriptive sampling guidelines

B Mildenhall, PP Srinivasan, R Ortiz-Cayon… - ACM Transactions on …, 2019 - dl.acm.org
We present a practical and robust deep learning solution for capturing and rendering novel
views of complex real world scenes for virtual exploration. Previous approaches either …

Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges

D Feng, C Haase-Schütz, L Rosenbaum… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …