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In 3D reconstruction, the obtained surface details are mainly limited to the visual sensor due to sampling and quantization in the digitalization process. How to get a fine-grained 3D surface with low-cost is still a challenging obstacle in terms of experience, equipment and easyto-obtain. This work introduces a novel framework for enhancing surfaces reconstructed from normal map, where the assumptions...
We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based...
We present an algorithm to compute parametric models of dense foliage. The guiding principles of our work are automatic reconstruction and compact artist friendly representation. We use Bezier patches to model leaf surface, which we compute from images and point clouds of dense foliage. We present an algorithm to segment individual leaves from colour and depth data. We then reconstruct the Bezier...
We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated...
Significant progress has been recently made in Non-Rigid Structure-from-Motion (NRSfM). However, existing methods do not handle poorly-textured surfaces that deform non-smoothly. These are nonetheless common occurrence in real-world applications. An important unanswered question is whether shading can be used to robustly handle these cases. Shading is complementary to motion because it constrains...
This paper introduces a new method for real-time shape sensing. Using a single inertial measurement unit (IMU), our method enables to scan physical objects and to reconstruct digital 3D models. By moving the IMU along the surface, a network of local orientation data is acquired together with traveled distances and network topology. We then reconstruct a consistent network of curves and fit these curves...
This paper describes the new method for measuring the shape of the front surface and the rear surface of a transparent object is difficult because a light does not reflect largely on the surface but mostly penetrate in the object. The proposed use the slightly reflected light on the surface of the transparent object. The experimental results show the usefulness of the proposed method for shape measurement...
The common challenge of all aerospace advancements is greening the air transport. This led the recent research programs towards the study of “metamorphic” wing structures, capable of adapting their geometry to the different conditions of flight. The development of morphing structures allows the reduction of drag and the increase of range, together with the growth of load control effectiveness. In...
The three-dimensional reconstruction of scenes from multiple views has made impressive strides in recent years, chiefly by methods correlating isolated feature points, intensities, or curvilinear structure. In the general setting, i.e., without requiring controlled acquisition, limited number of objects, abundant patterns on objects, or object curves to follow particular models, the majority of these...
We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors. To this end, we tackle the problem of non-rigid registration by level set evolution without explicit correspondence search. Given a pair of signed distance fields (SDFs) representing the shapes of interest, we estimate a dense deformation field...
3D shape models are naturally parameterized using vertices and faces, i.e., composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized representation of the object. Lifting convolution operators from the traditional 2D to 3D results in high computational overhead with little additional...
Highly effective optimization frameworks have been developed for traditional multiview stereo relying on lambertian photoconsistency. However, they do not account for complex material properties. On the other hand, recent works have explored PDE invariants for shape recovery with complex BRDFs, but they have not been incorporated into robust numerical optimization frameworks. We present a variational...
Multi-view stereo relies on feature correspondences for 3D reconstruction, and thus is fundamentally flawed in dealing with featureless scenes. In this paper, we propose polarimetric multi-view stereo, which combines per-pixel photometric information from polarization with epipolar constraints from multiple views for 3D reconstruction. Polarization reveals surface normal information, and is thus helpful...
3D Reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision. Unlike previous approaches that reconstruct each surface point independently and often return noisy depth maps, we propose a novel global optimization-based approach that recovers both depths and normals of all 3D points simultaneously. Using the traditional refraction stereo setup, we capture the wavy...
Three-dimensional surface reconstruction based on photometric stereo requires accurate positioning of the light source or estimation of the lighting parameters, which increases the complexity of the operation in experiments. Moreover, the actual light environment is composed of many kinds of complex optical components, and the accurate lighting parameters cannot be measured. In this paper, a reference...
We present a novel global registration method for deformable objects captured using a single RGB-D camera. Our algorithm allows objects to undergo large non-rigid deformations, and achieves high quality results without constraining the actor's pose or camera motion. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure...
Specular highlights provide information about the shape of an object. Its characteristics are mostly unwanted in computer vision due to violation of the Lambertian assumption, which most algorithms require. Instead of neglecting this ubiquitous phenomenon we harvest it to extract surface normals with very high accuracy. Compared to photometric stereo our method works with multiple views and a fixed...
Could bandwidth—one of the most classic concepts in signal processing—have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our attention to orthographic projection and assume a 2-D world.
The most standard approach to resolve the inherent ambiguities of the non-rigid structure from motion problem is using low-rank models that approximate deforming shapes by a linear combination of rigid basis. These models are typically global, i.e., each shape basis contributes equally to all points of the surface. While this approach has been shown effective to represent smooth deformations, its...
The concept of generalized B-spline basis function was extended by considering monotone increasing continuously differentiable nonlinear core functions instead of the classical linear one. It is not only compatible with classical B-spline basis function but also provides plentiful curves and surfaces for geometric modeling system. This paper takes generalized B-splines as examples and focuses on G2...
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