Field-aligned online surface reconstruction

Abstract

Today's 3D scanning pipelines can be classified into two overarching categories: ofine, high accuracy methods that rely on global optimization to reconstruct complex scenes with hundreds of millions of samples, and online methods that produce real-time but low-quality output, usually from structure-from-motion or depth sensors. The method proposed in this paper is the first to combine the benefits of both approaches, supporting online reconstruction of scenes with hundreds of millions of samples from high-resolution sensing modalities such as structured light or laser scanners. The key property of our algorithm is that it sidesteps the signed-distance computation of classical reconstruction techniques in favor of direct filtering, parametrization, and mesh and texture extraction. All of these steps can be realized using only weak notions of spatial neighborhoods, which allows for an implementation that scales approximately linearly with the size of each dataset that is integrated into a partial reconstruction. Combined, these algorithmic differences enable a drastically more efficient output-driven interactive scanning and reconstruction workflow, where the user is able to see the final quality field-aligned textured mesh during the entirety of the scanning procedure. Holes or parts with registration problems are displayed in real-time to the user and can be easily resolved by adding further localized scans, or by adjusting the input point cloud using our interactive editing tools with immediate visual feedback on the output mesh. We demonstrate the effectiveness of our algorithm in conjunction with a state-of-the-art structured light scanner and optical tracking system and test it on a large variety of challenging models

Be water my friend: mesh assimilation

“…Our approach is quite invariant to these artifacts, as it is able to cope with noise and can either ignore or incorporate varying sample densities. Impressing developments in quad-mesh reconstructions such as Instant Field Aligned Meshes (IFAM) [23] or Online Surface Reconstruction (OSR) [34] produce nice feature-aligned mesh structures, but often struggle with low or varying sample density as demonstrated in comparison with our results. Most recently, learning-based approaches also joined the reconstruction game [32,33] but often require specifically trained models on an explicit object class.…”

Accuracy analysis of surface reconstruction from point clouds

“…Extensive experiments revealed that very good denoising results cannot be achieved using the following filters: MB [22], PSSRVTK [23], PSRVTK [24], BSR [25], RTM [26], ISR [27], FAOSR [28]. The main reason is incorrect mesh after surface reconstruction, therefore, we do not use these algorithms for our next experiments and comparisons.…”

“…The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of the following surface reconstruction algorithms: the marching cubes (MB) [22], the point set surface reconstruction for VTK (PSSRVTK) [23], the Poisson surface reconstruction algorithm in the VTK framework (PSRVTK) [24], the implicit hierarchical B-Splines surface reconstruction based on octree distance field (BSR) [25], the reconstruction of a triangle mesh from a set of oriented 3D points by solving a Poisson system (RTM) [26], the interactive surface reconstruction software with curve-driven controls (ISR) [27], the field-aligned online surface reconstruction (FAOSR) [28], the Ball-pivoting algorithm for surface reconstruction (BPSR) [29], the Poisson surface reconstruction (PSR) [30], the Power Crust algorithm (PC) [31].…”

A Photogrammetry-Based Workflow for the Accurate 3D Construction and Visualization of Museums Assets

“…Quad re-meshing [62] and retopology provide two alternative systems to get these couples of orthogonal directions while guaranteeing their consistency with local orientation, since they are associated with principal curvature directions and sharp features. Over the last years, quad-dominant meshes gained increasing popularity also in the realitybased modelling field [63,64], for their ability to generate shapes derived from non-contact capture processes more suitable for the computational processes. The quad-dominant meshes can be easily converted into models with different LoD, allowing better rendering of elements under morphing and easier parameterization [65] and enhanced lighting simulations, since the final re-meshed surface has cells aligned with a pair of directions.…”