A Novel Technique for Point-wise Surface Normal Estimation (original) (raw)
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Optimal Multi-View Surface Normal Estimation using Affine Correspondences
IEEE Transactions on Image Processing
An optimal, in the least squares sense, method is proposed to estimate surface normals in both stereo and multi-view cases. The proposed algorithm exploits exclusively photometric information via affine correspondences and estimates the normal for each correspondence independently. The normal is obtained as a root of a quartic polynomial. Therefore, the processing time is negligible. Eliminating the outliers, we propose a robust extension of the algorithm that combines maximum likelihood estimation and iteratively re-weighted least squares. The method has been validated on both synthetic and publicly available real-world datasets. It is superior to the state of the art in terms of accuracy and processing time. Besides, we demonstrate two possible applications: 1) using our algorithm as the seed-point generation step of patch-based multi-view stereo method, the obtained reconstruction is more accurate, and the error of the 3D points is reduced by 30% on average and 2) multiplane fitting becomes more accurate applied to the resulting oriented point cloud.
Accurate stereo 3D point cloud generation suitable for multi-view stereo reconstruction
2014 IEEE Visual Communications and Image Processing Conference, 2014
This paper proposes a novel methodology for generating 3D point clouds of good accuracy from stereo pairs. Initially, the methodology defines some conditions for the proper selection of image pairs. Then, the selected stereo images are used to estimate dense correspondences using the Daisy descriptor. An efficient two-phase strategy to remove outliers is then introduced. Finally, the 3D point cloud is refined by combining sub-pixel accuracy correspondences estimation and the moving least squares algorithm. The proposed methodology can be exploited by multiview stereo algorithms due to its good accuracy and its fast computation.
Surface–Normal Estimation with Neighborhood Reorganization for 3D Reconstruction
Lecture Notes in Computer Science, 2008
Fastest three-dimensional (3D) surface reconstruction algorithms, from point clouds, require of the knowledge of the surface-normals. The accuracy, of state of the art methods, depends on the precision of estimated surface-normals. Surface-normals are estimated by assuming that the surface can be locally modelled by a plane as was proposed by Hoppe et. al . Thus, current methods for estimating surface-normals are prone to introduce artifacts at the geometric edges or corners of the objects. In this paper an algorithm for Normal Estimation with Neighborhood Reorganization (NENR) is presented. Our proposal changes the characteristics of the neighborhood in places with corners or edges by assuming a locally plane piecewise surface. The results obtained by NENR improve the quality of the normal with respect to the state of the art algorithms. The new neighborhood computed by NENR, use only those points that belong to the same plane and they are the nearest neighbors. Experiments in synthetic and real data shown an improvement on the geometric edges of 3D reconstructed surfaces when our algorithm is used.
Multimedia Tools and Applications, 2018
Sparse 3D reconstruction, based on interest points detection and matching, does not allow to obtain a suitable 3D surface reconstruction because of its incapacity to recover a cloud of well distributed 3D points on the surface of objects/scenes. In this work, we present a new approach to retrieve a 3D point cloud that leads to a 3D surface model of quality and in a suitable time. First of all, our method uses the structure from motion approach to retrieve a set of 3D points (which correspond to matched interest points). After that, we proposed an algorithm, based on the match propagation and the use of particle swarm optimization (PSO), which significantly increases the number of matches and to have a regular distribution of these matches. It takes as input the obtained matches, their corresponding 3D points and the camera parameters. Afterwards, at each time, a match of best ZNCC value is selected and a set of these neighboring points is defined. The point corresponding to a neighboring point and its 3D coordinates are recovered by the minimization of a nonlinear cost function by the use of PSO algorithm respecting the constraint of photo-consistency. Experimental results show the feasibility and efficiency of the proposed approach.
Using point correspondences without projective deformation for multi-view stereo reconstruction
2008 15th IEEE International Conference on Image Processing, 2008
This paper proposes a novel algorithm to reconstruct a 3D surface from a calibrated set of images. In a first pass, it uses Scale Invariant Features Transform (SIFT) descriptor correspondences to drive the deformation of a mesh toward the true object surface. We introduce a method to handle the fact that these local descriptors are computed at positions that are not projections of mesh vertices in the images. In order to avoid projective deformations due to the large windows of interest of this descriptor, correspondences are only computed between images from the same viewpoint. This is used in a first pass to recover large concavities of the object. In a second pass, a one dimensional Lucas-Kanade tracker is used to recover small scale details. Using publicly available benchmarks, our algorithm obtains high accuracy while being among the fastest ones.
Robotics and Autonomous Systems, 2014
This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner. © 2014 Elsevier B.V. All rights reserved.
Fast and accurate multi-view reconstruction by multi-stage prioritised matching
IET Computer Vision, 2015
In this paper, we propose a multi-view stereo reconstruction method which creates a three-dimensional point cloud of a scene from multiple calibrated images captured from different viewpoints. The method is based on a prioritized match expansion technique, which starts from a sparse set of seed points, and iteratively expands them into neighboring areas by using multiple expansion stages. Each seed point represents a surface patch and has a position and a surface normal vector. The location and surface normal of the seeds are optimized using a homography-based local image alignment. The propagation of seeds is performed in a prioritized order in which the most promising seeds are expanded first and removed from the list of seeds. The first expansion stage proceeds until the list of seeds is empty. In the following expansion stages, the current reconstruction may be further expanded by finding new seeds near the boundaries of the current reconstruction. The prioritized expansion strategy allows efficient generation of accurate point clouds and our experiments show its benefits compared with nonprioritized expansion. In addition, a comparison to the widely used patch-based multi-view stereo software (PMVS) shows that our method is significantly faster and produces more accurate and complete reconstructions.
Improved Poisson Surface Reconstruction with Various Passive Visual Cues from Multiple Camera Views
Poisson surface reconstruction with octree is widely used as the last step to retrieve the surface data from the point cloud. When the point cloud is generated by the triangulation of point correspondence in multiple images, the noisy positions of the 3D points and the inaccurate estimation of the normal vectors will impact the quality of the reconstructed surface. In this work, the mesh optimization using multiple visual cues will be applied to improve the output of the Poisson surface reconstruction. Usually, the active cues like shading and focusing require elaborate experimental setup, whereas the passive cues like silhouette and photometric property can be more easily acquired from the raw images. The experimental results show that adaptive integration of the multiple passive visual cues will deliver the surface mesh data with high quality. Besides, the optimization algorithm is easily to parallelize, as each vertex moves independently, which makes it appealing for the real-time 3D reconstruction system.
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
A novel surface normal estimator is introduced using affine-invariant features extracted and tracked across multiple views. Normal estimation is robustified and integrated into our reconstruction pipeline that has increased accuracy compared to the State-of-the-Art. Parameters of the views and the obtained spatial model, including surface normals, are refined by a novel bundle adjustment-like numerical optimization. The process is an alternation with a novel robust view-dependent consistency check for surface normals, removing normals inconsistent with the multipleview track. Our algorithms are quantitatively validated on the reverse engineering of geometrical elements such as planes, spheres, or cylinders. It is shown here that the accuracy of the estimated surface properties is appropriate for object detection. The pipeline is also tested on the reconstruction of man-made and free-form objects.