SurfelMeshing: Online Surfel-Based Mesh Reconstruction

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

초점 스택에서 3D 깊이 재구성 및 깊이 개선

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 신영길.Three-dimensional (3D) depth recovery from two-dimensional images is a fundamental and challenging objective in computer vision, and is one of the most important prerequisites for many applications such as 3D measurement, robot location and navigation, self-driving, and so on. Depth-from-focus (DFF) is one of the important methods to reconstruct a 3D depth in the use of focus information. Reconstructing a 3D depth from texture-less regions is a typical issue associated with the conventional DFF. Further more, it is difficult for the conventional DFF reconstruction techniques to preserve depth edges and fine details while maintaining spatial consistency. In this dissertation, we address these problems and propose an DFF depth recovery framework which is robust over texture-less regions, and can reconstruct a depth image with clear edges and fine details. The depth recovery framework proposed in this dissertation is composed of two processes: depth reconstruction and depth refinement. To recovery an accurate 3D depth, We first formulate the depth reconstruction as a maximum a posterior (MAP) estimation problem with the inclusion of matting Laplacian prior. The nonlocal principle is adopted during the construction stage of the matting Laplacian matrix to preserve depth edges and fine details. Additionally, a depth variance based confidence measure with the combination of the reliability measure of focus measure is proposed to maintain the spatial smoothness, such that the smooth depth regions in initial depth could have high confidence value and the reconstructed depth could be more derived from the initial depth. As the nonlocal principle breaks the spatial consistency, the reconstructed depth image is spatially inconsistent. Meanwhile, it suffers from texture-copy artifacts. To smooth the noise and suppress the texture-copy artifacts introduced in the reconstructed depth image, we propose a closed-form edge-preserving depth refinement algorithm that formulates the depth refinement as a MAP estimation problem using Markov random fields (MRFs). With the incorporation of pre-estimated depth edges and mutual structure information into our energy function and the specially designed smoothness weight, the proposed refinement method can effectively suppress noise and texture-copy artifacts while preserving depth edges. Additionally, with the construction of undirected weighted graph representing the energy function, a closed-form solution is obtained by using the Laplacian matrix corresponding to the graph. The proposed framework presents a novel method of 3D depth recovery from a focal stack. The proposed algorithm shows the superiority in depth recovery over texture-less regions owing to the effective variance based confidence level computation and the matting Laplacian prior. Additionally, this proposed reconstruction method can obtain a depth image with clear edges and fine details due to the adoption of nonlocal principle in the construct]ion of matting Laplacian matrix. The proposed closed-form depth refinement approach shows that the ability in noise removal while preserving object structure with the usage of common edges. Additionally, it is able to effectively suppress texture-copy artifacts by utilizing mutual structure information. The proposed depth refinement provides a general idea for edge-preserving image smoothing, especially for depth related refinement such as stereo vision. Both quantitative and qualitative experimental results show the supremacy of the proposed method in terms of robustness in texture-less regions, accuracy, and ability to preserve object structure while maintaining spatial smoothness.Chapter 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 Related Works 9 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Principle of depth-from-focus . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Focus measure operators . . . . . . . . . . . . . . . . . . . 12 2.3 Depth-from-focus reconstruction . . . . . . . . . . . . . . . . . . 14 2.4 Edge-preserving image denoising . . . . . . . . . . . . . . . . . . 23 Chapter 3 Depth-from-Focus Reconstruction using Nonlocal Matting Laplacian Prior 38 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Image matting and matting Laplacian . . . . . . . . . . . . . . . 40 3.3 Depth-from-focus . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Depth reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . 47 3.4.2 Likelihood model . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.3 Nonlocal matting Laplacian prior model . . . . . . . . . . 50 3.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.2 Data configuration . . . . . . . . . . . . . . . . . . . . . . 55 3.5.3 Reconstruction results . . . . . . . . . . . . . . . . . . . . 56 3.5.4 Comparison between reconstruction using local and nonlocal matting Laplacian . . . . . . . . . . . . . . . . . . . 56 3.5.5 Spatial consistency analysis . . . . . . . . . . . . . . . . . 59 3.5.6 Parameter setting and analysis . . . . . . . . . . . . . . . 59 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Chapter 4 Closed-form MRF-based Depth Refinement 63 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3 Closed-form solution . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4 Edge preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.5 Texture-copy artifacts suppression . . . . . . . . . . . . . . . . . 73 4.6 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Chapter 5 Evaluation 82 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.3 Evaluation on synthetic datasets . . . . . . . . . . . . . . . . . . 84 5.4 Evaluation on real scene datasets . . . . . . . . . . . . . . . . . . 89 5.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.6 Computational performances . . . . . . . . . . . . . . . . . . . . 93 Chapter 6 Conclusion 96 Bibliography 99Docto

Feature preserving variational smoothing of terrain data

Journal ArticleIn this paper, we present a novel two-step, variational and feature preserving smoothing method for terrain data. The first step computes the field of 3D normal vectors from the height map and smoothes them by minimizing a robust penalty function of curvature. This penalty function favors piecewise planar surfaces; therefore, it is better suited for processing terrain data then previous methods which operate on intensity images. We formulate the total curvature of a height map as a function of its normals. Then, the gradient descent minimization is implemented with a second-order partial differential equation (PDE) on the field of normals. For the second step, we define another penalty function that measures the mismatch between the the 3D normals of a height map model and the field of smoothed normals from the first step. Then, starting with the original height map as the initialization, we fit a non-parametric terrain model to the smoothed normals minimizing this penalty function. This gradient descent minimization is also implemented with a second-order PDE. We demonstrate the effectiveness of our approach with a ridge/gully detection application