66 research outputs found
Planar Prior Assisted PatchMatch Multi-View Stereo
The completeness of 3D models is still a challenging problem in multi-view
stereo (MVS) due to the unreliable photometric consistency in low-textured
areas. Since low-textured areas usually exhibit strong planarity, planar models
are advantageous to the depth estimation of low-textured areas. On the other
hand, PatchMatch multi-view stereo is very efficient for its sampling and
propagation scheme. By taking advantage of planar models and PatchMatch
multi-view stereo, we propose a planar prior assisted PatchMatch multi-view
stereo framework in this paper. In detail, we utilize a probabilistic graphical
model to embed planar models into PatchMatch multi-view stereo and contribute a
novel multi-view aggregated matching cost. This novel cost takes both
photometric consistency and planar compatibility into consideration, making it
suited for the depth estimation of both non-planar and planar regions.
Experimental results demonstrate that our method can efficiently recover the
depth information of extremely low-textured areas, thus obtaining high complete
3D models and achieving state-of-the-art performance.Comment: Accepted by AAAI-202
MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo
Significant strides have been made in enhancing the accuracy of Multi-View
Stereo (MVS)-based 3D reconstruction. However, untextured areas with unstable
photometric consistency often remain incompletely reconstructed. In this paper,
we propose a resilient and effective multi-view stereo approach (MP-MVS). We
design a multi-scale windows PatchMatch (mPM) to obtain reliable depth of
untextured areas. In contrast with other multi-scale approaches, which is
faster and can be easily extended to PatchMatch-based MVS approaches.
Subsequently, we improve the existing checkerboard sampling schemes by limiting
our sampling to distant regions, which can effectively improve the efficiency
of spatial propagation while mitigating outlier generation. Finally, we
introduce and improve planar prior assisted PatchMatch of ACMP. Instead of
relying on photometric consistency, we utilize geometric consistency
information between multi-views to select reliable triangulated vertices. This
strategy can obtain a more accurate planar prior model to rectify photometric
consistency measurements. Our approach has been tested on the ETH3D High-res
multi-view benchmark with several state-of-the-art approaches. The results
demonstrate that our approach can reach the state-of-the-art. The associated
codes will be accessible at https://github.com/RongxuanTan/MP-MVS
Semantically Derived Geometric Constraints for {MVS} Reconstruction of Textureless Areas
Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints during multiview stereo, optimising the depth estimation on weakly supported, textureless areas, commonly present in urban scenarios of building facades, indoor scenes, or aerial datasets. Detecting dominant shapes, e.g., planes, with RANSAC, an adjusted cost function is introduced that combines and weighs both photometric and semantic scores propagating, thus, more accurate depth estimates. Being adaptive, it fills in apparent information gaps and smoothing local roughness in problematic regions while at the same time preserves important details. Experiments on benchmark and custom datasets demonstrate the effectiveness of the presented approach
Polarimetric PatchMatch Multi-View Stereo
PatchMatch Multi-View Stereo (PatchMatch MVS) is one of the popular MVS
approaches, owing to its balanced accuracy and efficiency. In this paper, we
propose Polarimetric PatchMatch multi-view Stereo (PolarPMS), which is the
first method exploiting polarization cues to PatchMatch MVS. The key of
PatchMatch MVS is to generate depth and normal hypotheses, which form local 3D
planes and slanted stereo matching windows, and efficiently search for the best
hypothesis based on the consistency among multi-view images. In addition to
standard photometric consistency, our PolarPMS evaluates polarimetric
consistency to assess the validness of a depth and normal hypothesis, motivated
by the physical property that the polarimetric information is related to the
object's surface normal. Experimental results demonstrate that our PolarPMS can
improve the accuracy and the completeness of reconstructed 3D models,
especially for texture-less surfaces, compared with state-of-the-art PatchMatch
MVS methods
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction
Deep Neural Networks (DNNs) have the potential to improve the quality of
image-based 3D reconstructions. However, the use of DNNs in the context of 3D
reconstruction from large and high-resolution image datasets is still an open
challenge, due to memory and computational constraints. We propose a pipeline
which takes advantage of DNNs to improve the quality of 3D reconstructions
while being able to handle large and high-resolution datasets. In particular,
we propose a confidence prediction network explicitly tailored for Multi-View
Stereo (MVS) and we use it for both depth map outlier filtering and depth map
refinement within our pipeline, in order to improve the quality of the final 3D
reconstructions. We train our confidence prediction network on (semi-)dense
ground truth depth maps from publicly available real world MVS datasets. With
extensive experiments on popular benchmarks, we show that our overall pipeline
can produce state-of-the-art 3D reconstructions, both qualitatively and
quantitatively.Comment: changes in V3: re-worked confidence prediction scheme, re-organized
text, updated experiments; changes in V2: a reference was update
Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching
The performance of PatchMatch-based multi-view stereo algorithms depends
heavily on the source views selected for computing matching costs. Instead of
modeling the visibility of different views, most existing approaches handle
occlusions in an ad-hoc manner. To address this issue, we propose a novel
visibility-guided pixelwise view selection scheme in this paper. It
progressively refines the set of source views to be used for each pixel in the
reference view based on visibility information provided by already validated
solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is
employed to search for optimal solutions for different pixels in parallel.
Inter-colony communication is performed both within the same image and among
different images. Fitness rewards are added to validated and propagated
solutions, effectively enforcing the smoothness of neighboring pixels and
allowing better handling of textureless areas. Experimental results on the DTU
dataset show our method achieves state-of-the-art performance among
non-learning-based methods and retrieves more details in occluded and
low-textured regions.Comment: 8 page
PatchMatch Belief Propagation for Correspondence Field Estimation and its Applications
Correspondence fields estimation is an important process that lies at the core of many different applications. Is it often seen as an energy minimisation problem, which is usually decomposed into the combined minimisation of two energy terms. The first is the unary energy, or data term, which reflects how well the solution agrees with the data. The second is the pairwise energy, or smoothness term, and ensures that the solution displays a certain level of smoothness, which is crucial for many applications. This thesis explores the possibility of combining two well-established algorithms for correspondence field estimation, PatchMatch and Belief Propagation, in order to benefit from the strengths of both and overcome some of their weaknesses. Belief Propagation is a common algorithm that can be used to optimise energies comprising both unary and pairwise terms. It is however computational expensive and thus not adapted to continuous spaces which are often needed in imaging applications. On the other hand, PatchMatch is a simple, yet very efficient method for optimising the unary energy of such problems on continuous and high dimensional spaces. The algorithm has two main components: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these components are related to the components of a specific form of Belief Propagation, called Particle Belief Propagation (PBP). PatchMatch however suffers from the lack of an explicit smoothness term. We show that unifying the two approaches yields a new algorithm, PMBP, which has improved performance compared to PatchMatch and is orders of magnitude faster than PBP. We apply our new optimiser to two different applications: stereo matching and optical flow. We validate the benefits of PMBP through series of experiments and show that we consistently obtain lower errors than both PatchMatch and Belief Propagation
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