Truncated Signed Distance Function: Experiments on Voxel Size

Werner, Diana; Al-Hamadi, Ayoub; Werner, Philipp

doi:10.1007/978-3-319-11755-3_40

Cited by 29 publications

(11 citation statements)

References 8 publications

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“…Some of them require dense point clouds as an intermediate step while generating the surface model [ 36 , 37 ]. Other volumetric methods, such as the so-called Truncated Signed Distance Field algorithm (TSDF), use straightaway depth maps and generate a surface by dividing the 3D space into 3D voxel cells where each voxel is labeled with a distance [ 38 ]. However, while surface reconstruction from depth maps is quite common when using RGB-D sensors [ 15 , 39 ], in image-based 3D applications of metric accuracy it is still not fully exploited.…”

Section: On Dim and Mvsmentioning

confidence: 99%

Surface Reconstruction Assessment in Photogrammetric Applications

Nocerino

Stathopoulou

Rigon

et al. 2020

Sensors

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The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation can be considered as a problem integrated in the complete 3D reconstruction workflow and thus visibility information (pixel similarity and image orientation) is leveraged in the meshing procedure contributing to an optimal photo-consistent mesh. Other methods tackle the problem as an independent and subsequent step, generating a mesh model starting from a dense 3D point cloud or even using depth maps, discarding input image information. Out of the vast number of approaches for 3D surface generation, in this study, we considered three state of the art methods. Experiments were performed on benchmark and proprietary datasets of varying nature, scale, shape, image resolution and network designs. Several evaluation metrics were introduced and considered to present qualitative and quantitative assessment of the results.

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Section: On Dim and Mvsmentioning

confidence: 99%

Surface Reconstruction Assessment in Photogrammetric Applications

Nocerino

Stathopoulou

Rigon

et al. 2020

Sensors

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“…A positive or zero signed distance indicates that the voxel x is inside the surface, whereas a negative signed distance indicates that the voxel x is outside the surface. Since large signed distances are irrelevant in surface reconstruction [12], s i ( x ) can be truncated using d trunc so that d i ( x ) ranges from − d trunc to + d trunc .…”

Section: Truncated Signed Distance Field Fusion and Marching Cube Alg...mentioning

confidence: 99%

Voxel‐wise UV parameterization and view‐dependent texture synthesis for immersive rendering of truncated signed distance field scene model

Kim

Kang

2022

ETRI Journal

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“…Volume-based methods (Curless and Levoy, 1996) discretize the space into voxels or tetrahedra and calculate a 3D volume from which the optimal surface will be extracted using e.g. graph-cuts (Vogiatzis et al, 2007) or the signed distance function (Newcombe et al, 2011;Werner et al, 2014). Surfaces are produced from volumetric representations using Poisson triangulation (Kazhdan et al, 2006) or the marching-cubes algorithm (Lorensen and Cline, 1987).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Geometric Edge Details in MVS Reconstruction

Stathopoulou

Rigon

Battisti

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection and matching are commonly exploited either to constrain camera pose estimation, or to generate an abstract representation of the most salient parts of the scene, and even to support mesh reconstruction. In this work, we attempt to jointly use 3D edge extraction and MVS mesh generation to promote edge detail preservation in the final result. Salient 3D edges of the scene are reconstructed with state-of-the-art algorithms and integrated in the dense point cloud to be further used in order to support the mesh triangulation step. Experimental results on benchmark dataset sequences using metric and appearance-based measures are performed in order to evaluate our hypothesis.

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Truncated Signed Distance Function: Experiments on Voxel Size

Cited by 29 publications

References 8 publications

Surface Reconstruction Assessment in Photogrammetric Applications

Surface Reconstruction Assessment in Photogrammetric Applications

Voxel‐wise UV parameterization and view‐dependent texture synthesis for immersive rendering of truncated signed distance field scene model

Enhancing Geometric Edge Details in MVS Reconstruction

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