A review of 3D reconstruction from high-resolution urban satellite images

Li, Zhao; Wang, Haiyan; Zhu, Yi; Song, Mei

doi:10.1080/01431161.2023.2169844

Cited by 21 publications

(5 citation statements)

References 143 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the above equation, the value of 𝛾 is 48, and the other parameters are the same as in Equation (10). The weights of the grayscale criterion and the semantic criterion are 0.8 and 0.2, which are summarized through the experiments.…”

Section: Cost Aggregationmentioning

confidence: 99%

“…With advancements in fields such as autonomous driving [1], digital twins [2,3], cultural heritage preservation [4], and humanities research and education [5], the importance of 3D reconstruction has become increasingly paramount. There are numerous methods for 3D reconstruction, including active reconstruction techniques like LiDAR, synthetic aperture radar (SAR), Time-of-Flight (TOF), and structured light cameras, as well as passive reconstruction methods based on vision cameras [6][7][8][9][10]. Compared to active reconstruction methods such as LiDAR and TOF, visual 3D reconstruction offers several advantages including ease of use, richness of information, and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications

Wei,

Pei,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial challenges in attaining 3D reconstructions with high precision. In this paper, we propose a semantically aware multi-view 3D reconstruction method for urban applications which incorporates semantic information into the technical 3D reconstruction. Our research primarily focuses on two major components: sparse reconstruction and dense reconstruction. For the sparse reconstruction process, we present a semantic consistency-based error filtering approach for feature matching. To address the challenge of errors introduced by the presence of numerous dynamic objects in an urban scene, which affects the Structure-from-Motion (SfM) process, we propose a computation strategy based on dynamic–static separation to effectively eliminate mismatches. For the dense reconstruction process, we present a semantic-based Semi-Global Matching (sSGM) method. This method leverages semantic consistency to assess depth continuity, thereby enhancing the cost function during depth estimation. The improved sSGM method not only significantly enhances the accuracy of reconstructing the edges of the targets but also yields a dense point cloud containing semantic information. Through validation using architectural datasets, the proposed method was found to increase the reconstruction accuracy by 32.79% compared to the original SGM, and by 63.06% compared to the PatchMatch method. Therefore, the proposed reconstruction method holds significant potential in urban applications.

show abstract

Section: Cost Aggregationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications

Wei,

Pei,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…With the advancement of high-resolution sensors in the field of remote sensing [1,2], the horizontal pixel resolution (the horizontal distance represented by a single pixel) of the visible light band for ground observation from satellite/aerial platforms has reached the level of decimeters or even centimeters. This has made various downstream applications such as fine-grained urban 3D reconstruction [3], high-precision mapping [4], and MR scene interaction [5] incrementally achievable. Among these applications, the ground surface height value (Digital Surface Model, DSM) serves as a primary data support, and its acquisition methods have been a focal point of research [6].…”

Section: Introductionmentioning

confidence: 99%

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Chen,

Zhang,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification–regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification–regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification–regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.

show abstract

“…As high-resolution satellite images play an increasingly important role in geospatial cognition, using these images to perform large-scale 3D models of the Earth's surface has become a research hotspot in the fields of remote sensing and photogrammetry [1][2][3][4]. Compared with traditional stereo or tri-stereo photogrammetry methods and the use of active sensor technologies such as lidar, 3D reconstruction methods based on multi-view satellite images are widely used in urban mapping [5,6], environmental monitoring [7], navigation [8], and emergency rescue [9,10] due to their advantages in terms of easily accessible data, high collection efficiency, and low cost.…”

Section: Introductionmentioning

confidence: 99%

SatelliteRF: Accelerating 3D Reconstruction in Multi-View Satellite Images with Efficient Neural Radiance Fields

Zhou,

Wang,

Lin

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In the field of multi-view satellite photogrammetry, the neural radiance field (NeRF) method has received widespread attention due to its ability to provide continuous scene representation and realistic rendering effects. However, the satellite radiance field methods based on the NeRF are limited by the slow training speed of the original NeRF, and the scene reconstruction efficiency is low. Training for a single scene usually takes 8–10 h or even longer, which severely constrains the utilization and exploration of the NeRF approach within the domain of satellite photogrammetry. In response to the above problems, we propose an efficient neural radiance field method called SatelliteRF, which aims to quickly and efficiently reconstruct the earth’s surface through multi-view satellite images. By introducing innovative multi-resolution hash coding, SatelliteRF enables the model to greatly increase the training speed while maintaining high reconstruction quality. This approach allows for smaller multi-layer perceptron (MLP) networks, reduces the computational cost of neural rendering, and accelerates the training process. Furthermore, to overcome the challenges of illumination changes and transient objects encountered when processing multi-date satellite images, we adopt an improved irradiance model and learn transient embeddings for each image. This not only increases the adaptability of the model to illumination variations but also improves its ability to handle changing objects. We also introduce a loss function based on stochastic structural similarity (SSIM) to provide structural information of the scene for model training, which further improves the quality and detailed performance of the reconstructed scene. Through extensive experiments on the DFC 2019 dataset, we demonstrate that SatelliteRF is not only able to significantly reduce the training time for the same region from the original 8–10 h to only 5–10 min but also achieves better performance in terms of rendering and the reconstruction quality.

show abstract

A review of 3D reconstruction from high-resolution urban satellite images

Cited by 21 publications

References 143 publications

A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications

A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

SatelliteRF: Accelerating 3D Reconstruction in Multi-View Satellite Images with Efficient Neural Radiance Fields

Contact Info

Product

Resources

About