3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions

Основні поняття

A multi-level supervised building reconstruction network (MLS-BRN) that can flexibly utilize training samples with different annotation levels to achieve better reconstruction results in an end-to-end manner.

Анотація

The paper proposes a multi-level supervised building reconstruction network (MLS-BRN) that can effectively utilize training samples with different annotation levels, including building footprint only, footprint and building height, and footprint, offset, and building height, to achieve better 3D building reconstruction performance.
Key highlights:

Designed two new modules, Pseudo Building Bbox Calculator (PBC) and Roof-Offset guided Footprint Extractor (ROFE), to alleviate the demand for full 3D supervision.
Introduced new tasks and training strategies for different types of samples to leverage the large-scale 2D footprint annotations and varying levels of 3D annotations.
Conducted experiments on several public and new datasets, demonstrating that the proposed MLS-BRN achieves competitive performance using much fewer 3D-annotated samples and significantly improves the footprint extraction and 3D reconstruction compared to state-of-the-art methods.

Статистика

Building height can be estimated from the image-wise off-nadir angle, offset angle, and the instance-wise building height using the equation: ||⃗vb||2 = hb × sI × tan(θI) × [cos(φI), sin(φI)].
The off-nadir angle prediction task has a mean absolute error (MAE) of 1.22 degrees when trained on BN100.
The offset angle prediction task has an MAE of 9.92 degrees when trained on BN100.

Цитати

"To alleviate the demand on 3D annotations and enhance the building reconstruction performance, we design new tasks regarding the meta information of off-nadir images and two new modules, i.e., Pseudo Building Bbox Calculator and Roof-Offset guided Footprint Extractor, as well as a new training strategy based on different types of samples."
"Experimental results on several public and new datasets demonstrate that our method achieves competitive performance when only using a small proportion of 3D-annotated samples, and significantly improves the building segmentation and height estimation performance compared with current state-of-the-art."

Ключові висновки, отримані з

3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions

by Weijia Li,Ha... о arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04823.pdf

Глибші Запити

How can the proposed MLS-BRN framework be extended to other 3D reconstruction tasks beyond building reconstruction?

The MLS-BRN framework can be extended to other 3D reconstruction tasks by adapting the network architecture and training strategies to suit the specific requirements of the new tasks. For example, for tasks such as terrain reconstruction or object reconstruction, the network can be modified to handle different types of shapes and structures. Additionally, the training data can be curated to include annotations relevant to the new tasks, such as ground elevation data for terrain reconstruction or object segmentation masks for object reconstruction. By adjusting the input data, network architecture, and training strategies, the MLS-BRN framework can be applied to a wide range of 3D reconstruction tasks beyond building reconstruction.

What are the potential limitations of the multi-level supervision approach, and how can they be addressed in future work?

One potential limitation of the multi-level supervision approach is the complexity of managing and integrating samples with different annotation levels. Ensuring that the network effectively learns from samples with varying levels of supervision can be challenging and may lead to issues such as information imbalance or conflicting signals. To address this limitation, future work could focus on developing more sophisticated training strategies, such as adaptive weighting of loss functions based on the annotation level of each sample. Additionally, incorporating techniques like self-supervised learning or semi-supervised learning could help in leveraging unlabeled data to improve the performance of the model.

Can the insights from this work on leveraging different levels of annotations be applied to improve 3D reconstruction in other domains, such as object or scene reconstruction?

Yes, the insights from leveraging different levels of annotations in 3D building reconstruction can be applied to improve 3D reconstruction in other domains such as object or scene reconstruction. By incorporating multi-level supervision and designing flexible frameworks that can adapt to varying levels of annotation complexity, models can be trained more efficiently and effectively. For object reconstruction, this approach can help in handling objects with different levels of detail or complexity. In scene reconstruction, it can aid in reconstructing complex environments with varying levels of annotation availability. By tailoring the network architecture and training strategies to the specific requirements of each domain, the benefits of multi-level supervision can be extended to a wide range of 3D reconstruction tasks.