Results 61 to 70 of about 24,425 (182)
Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions
Sensors, 2020 Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve.
Ivan Nikolov, Claus Madsen
doaj +1 more source
TC-SfM: Robust Track-Community-Based Structure-From-Motion
IEEE Transactions on Image ProcessingStructure-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.
Lei Wang 0025 +5 more
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Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods [PDF]
Earth Surface Processes and Landforms, 2016 AbstractIn this study we evaluate the extent to which accurate topographic data can be obtained by applying Structure from Motion (SfM) photogrammetric methods to archival imagery. While SfM has proven valuable in photogrammetric applications using specially acquired imagery (e.g. from unmanned aerial vehicles), it also has the potential to improve the
Bakker Maarteen, Lane Stuart N.
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New metric products, movies and 3D models from old stereopairs and their application to the in situ palaeontological site of Ambrona [PDF]
, 2017 [ES] Este artículo está basado en la información del siguiente proyecto:● LDGP_mem_006-1: "[S_Ambrona_Insitu] Levantamiento fotogramétrico del yacimiento paleontológico “Museo in situ” de Ambrona (Soria)", http://hdl.handle.net/10810/7353● LDGP_mem_006-1:
Lopetegi Galarraga, Ane +5 more
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MASt3R-SfM: A Fully-Integrated Solution for Unconstrained Structure-from-Motion
2025 International Conference on 3D Vision (3DV)Structure-from-Motion (SfM), a task aiming at jointly recovering camera poses and 3D geometry of a scene given a set of images, remains a hard problem with still many open challenges despite decades of significant progress. The traditional solution for SfM consists of a complex pipeline of minimal solvers which tends to propagate errors and fails when ...
Bardienus Pieter Duisterhof +5 more
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Science & Justice, 2021 The recovery of three-dimensional footwear impressions at crime scenes can be a challenge but can also yield important investigative data. Traditional methods involve casting 3D impressions but these methods have limitations: the trace is usually destroyed during capture; the process can be time consuming, with a risk of failure; and the resultant cast
Hannah Larsen +2 more
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Learning Single-Image Depth from Videos using Quality Assessment Networks
, 2019 Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild.
Chen, Weifeng, Deng, Jia, Qian, Shengyi
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ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser ...
L. MacDonald +5 more
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MP-SfM: Monocular Surface Priors for Robust Structure-From-Motion
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)While Structure-from-Motion (SfM) has seen much progress over the years, state-of-the-art systems are prone to failure when facing extreme viewpoint changes in low-overlap, low-parallax or high-symmetry scenarios. Because capturing images that avoid these pitfalls is challenging, this severely limits the wider use of SfM, especially by non-expert users.
Zador Pataki +3 more
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SfM-TTR: Using Structure from Motion for Test-Time Refinement of Single-View Depth Networks
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Estimating a dense depth map from a single view is geometrically ill-posed, and state-of-the-art methods rely on learning depth's relation with visual appearance using deep neural networks. On the other hand, Structure from Motion (SfM) leverages multi-view constraints to produce very accurate but sparse maps, as matching across images is typically ...
Sergio Izquierdo, Javier Civera 0001
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