☆ 4.6 Article

Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

SENSORS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Electrical & Electronic

Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Sepideh Jalayer et al.

Summary: This study detected the temporal and spatial changes in land use and land cover patterns in the Chalus watershed over the last two decades using multitemporal Landsat images and predicted future changes for the year 2040. The findings revealed a significant increase in agricultural land and barren area, and a sharp decline in grassland and forest cover. The model predicted a continued decrease in forest cover and an expansion of barren area, agricultural land, and built-up area by 2040. Understanding the spatiotemporal dynamics of land use and land cover change is crucial in minimizing destructive consequences.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

添加到收藏夹

Article Computer Science, Information Systems

2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors

Monika Bansal et al.

Summary: The study focuses on object recognition in the field of image processing and computer vision, comparing the performance of feature descriptor algorithms and classification models. Hybridizing SIFT, SURF, and ORB with a Random Forest classification model achieves the best results for object recognition based on recognition accuracy, TPR, FPR, and AUC parameters.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

M3VSNET: UNSUPERVISED MULTI-METRIC MULTI-VIEW STEREO NETWORK

Baichuan Huang et al.

Summary: This paper introduces a novel unsupervised multi-metric MVS network (M(3)VSNet) for dense point cloud reconstruction, achieving better performance without supervision, improving reconstruction accuracy and continuity through a multi-metric loss function and normal-depth consistency.

2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Digging into Uncertainty in Self-supervised Multi-view Stereo

Hongbin Xu et al.

Summary: This study proposes a novel approach to improve the performance of self-supervised MVS by estimating epistemic uncertainty, addressing ambiguous supervision in the foreground and invalid supervision in the background.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Deep Multi-View Stereo Gone Wild

Francois Darmon et al.

Summary: This study examines the effectiveness of deep multi-view stereo methods when utilizing internet photo collections. Results show issues with unsupervised training on wild data, but supervised deep depthmap-based MVS methods perform exceptionally well in reconstructing a small number of internet images.

2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Self-supervised Learning of Depth Inference for Multi-view Stereo

Jiayu Yang et al.

Summary: The paper introduces a self-supervised learning framework for multi-view stereo, which leverages pseudo labels from input data to estimate depth information. Experimental results demonstrate that the proposed method outperforms existing unsupervised multi-view stereo networks on the DTU dataset.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

Jiayu Yang et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement

Zehao Yu et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Scale Geometric Consistency Guided Multi-View Stereo

Qingshan Xu et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

Ali Osman Ulusoy et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Article Computer Science, Software Engineering

Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction

Arno Knapitsch et al.

ACM TRANSACTIONS ON GRAPHICS (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis

Mengqi Ji et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Large-Scale Data for Multiple-View Stereopsis

Henrik Aanaes et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2016)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Progressive Prioritized Multi-view Stereo

Alex Locher et al.

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2016)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Massively Parallel Multiview Stereopsis by Surface Normal Diffusion

Silvano Galliani et al.

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Efficient large-scale multi-view stereo for ultra high-resolution image sets

Engin Tola et al.

MACHINE VISION AND APPLICATIONS (2012)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Accurate, Dense, and Robust Multiview Stereopsis

Yasutaka Furukawa et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2010)

添加到收藏夹

Article Computer Science, Software Engineering

PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing

Connelly Barnes et al.

ACM TRANSACTIONS ON GRAPHICS (2009)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.