Graph-based parallel large scale structure from motion

Yu, Chunshui; Shen, Shuhan; Chen, Yisong; Wang, Guoping

doi:10.1016/j.patcog.2020.107537

Cited by 39 publications

(50 citation statements)

References 15 publications

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“…To overcome this challenge, Cui et al [23] proposed a batched incremental SfM framework that contains two iteration loops: the inner loop that selects a well-conditioned subset of tracks and the outer loop that uses rotations estimated via rotation averaging as weak supervision for the registration. Chen et al [15] proposed a carefully designed clustering and merging algorithm to prevent the individual reconstructions from being affected by the wrong matches. Then, they performed a subgraph expansion step to enhance the connection and completeness of scenes.…”

Section: Related Workmentioning

confidence: 99%

“…However, this approach usually suffers from the drift problem in large-scale reconstruction [13], [14]. To overcome this drawback, many SfM methods adopt the strategy of first distributedly registering the cameras and then merging them [13], [15]. Considering that the whole scene has been divided into several parts in the previous disambiguation step, we also utilize the partitioning strategy for SfM.…”

Section: Introductionmentioning

confidence: 99%

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TC-SfM: Robust Track-Community-Based Structure-from-Motion

Wang¹,

Liu²,

Si³

et al. 2022

Preprint

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Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, track-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is used to partition the scene into several segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3D-3D correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TC-SfM: Robust Track-Community-Based Structure-from-Motion

Wang¹,

Liu²,

Si³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We used its incremental SfM pipeline. Details can be found at https://openmvg.readthedocs.io/en/latest/)) and two hierarchical methods (GraphSfM [19] (Code was downloaded from https://github.com/AIBluefisher/EGSfM. The version provided by the original author was implemented based on OpenMVG) and APE [29] (Note that we were unable to obtain the APE source code.…”

Section: Expansionmentioning

confidence: 99%

“…Third, triangulation to estimate 3D points suffers from limited robustness on match pairs stemming from very short baselines. Based on incremental methods, hierarchical SfM [16][17][18][19] creates atomic reconstructions in a divide-and-conquer manner and combines them hierarchically. While efficient due to the parallelization of dealing with atomic reconstructions, it is sensitive to the method by which these reconstructions grow.…”

Section: Introductionmentioning

confidence: 99%

Progressive Structure from Motion by Iteratively Prioritizing and Refining Match Pairs

Xiao

Qingsong

et al. 2021

Remote Sensing

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Structure from motion (SfM) has been treated as a mature technique to carry out the task of image orientation and 3D reconstruction. However, it is an ongoing challenge to obtain correct reconstruction results from image sets consisting of problematic match pairs. This paper investigated two types of problematic match pairs, stemming from repetitive structures and very short baselines. We built a weighted view-graph based on all potential match pairs and propose a progressive SfM method (PRMP-PSfM) that iteratively prioritizes and refines its match pairs (or edges). The method has two main steps: initialization and expansion. Initialization is developed for reliable seed reconstruction. Specifically, we prioritize a subset of match pairs by the union of multiple independent minimum spanning trees and refine them by the idea of cycle consistency inference (CCI), which aims to infer incorrect edges by analyzing the geometric consistency over cycles of the view-graph. The seed reconstruction is progressively expanded by iteratively adding new minimum spanning trees and refining the corresponding match pairs, and the expansion terminates when a certain completeness of the block is achieved. Results from evaluations on several public datasets demonstrate that PRMP-PSfM can successfully accomplish the image orientation task for datasets with repetitive structures and very short baselines and can obtain better or similar accuracy of reconstruction results compared to several state-of-the-art incremental and hierarchical SfM methods.

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“…Li et al (2008) clustered a set of images to find the 'iconic images' first, then computed 3D scene incrementally based on spanning trees. The studies of Cui et al (2020) and Chen et al (2020) can be pointed out in this field, too. The reduction of images makes large-scale adjustment more tractable, though the completeness of scene structure cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

Mousavi

Varshosaz

Remondino

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Image orientation is a fundamental task in photogrammetric applications and it is performed by extracting keypoints with hand-crafted or learning-based methods, generating tie points among the images and running a bundle adjustment procedure. Nowadays, due to large number of extracted keypoints, tie point filtering approaches attempt to eliminate redundant tie points in order to increase accuracy and reduce processing time. This paper presents the results of an investigation concerning tie points impact on bundle adjustment results. Simulations and real data are processed in Australis and DBAT to evaluate different affecting factors, including tie point numbers, location accuracy, distribution and multiplicity. Achieved results show that increasing the amount of tie points improve the quality of bundle adjustment results, provided that the tie points are well-distributed on the image. Furthermore, bundle adjustment quality is improved as the multiplicity of tie points increases and their location uncertainty decrease. Based on simulation results, some suggestions for accurate tie points filtering in typical UAV photogrammetry blocks cases are derived.

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Graph-based parallel large scale structure from motion

Cited by 39 publications

References 15 publications

TC-SfM: Robust Track-Community-Based Structure-from-Motion

TC-SfM: Robust Track-Community-Based Structure-from-Motion

Progressive Structure from Motion by Iteratively Prioritizing and Refining Match Pairs

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

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