Fast 3D Scene Segmentation and Partial Object Retrieval Using Local Geometric Surface Features

Dimou, Dimitrios; Μουστάκας, Κωνσταντίνος

doi:10.1007/978-3-662-61364-1_5

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Several factors lead to those measurements inaccuracies in the point clouds such as adverse weather conditions, e.g. fog [19]- [21] and rain [22], [23], objects reflective surface, the scanner itself [24]- [26], or by some pipelines that construct 3D objects from multi-view images [18], [27], [28]. Examples of those data inaccuracies can be seen in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Robust Object Classification Approach Using Spherical Harmonics

et al. 2022

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Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available 1 .INDEX TERMS object recognition, point cloud classification, spherical harmonics, robust classification.

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Section: Introductionmentioning

confidence: 99%

Robust Object Classification Approach Using Spherical Harmonics

et al. 2022

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“…Ohbuchi et al [35] combine the BoVW and the view-based paradigm by computing a bag of features over range images of an object rendered from different viewpoints, and comparing features of a query against those in a codebook. More recently, Dimou et al [19] used features computed from patches from segmented depth images.…”

Section: Partial Object Retrievalmentioning

confidence: 99%

“…sults from the referenced papers. Dimou et al [19] have also tested their work against this dataset, but no nearest neighbour retrieval performance was provided.…”

Section: Shrec'16 Partial Retrieval Performancementioning

confidence: 99%

Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

van Blokland,

Theoharis

2021

Preprint

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A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.

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On efficient 3D object retrieval

Liu,

Wong

2024

The VLDB Journal

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Due to the growth of the 3D technology, digital 3D models represented in the form of point clouds have attracted a lot of attention from both industry and academia. In this paper, due to a variety of applications, we study a fundamental problem called the 3D object retrieval, which is to find a set of 3D point clouds stored in a database that are similar to a given query 3D point cloud. To the best of our knowledge, solving the problem of 3D object retrieval efficiently remains unexplored in the research community. In this paper, we propose a framework called C$$_2$$ 2 O to find the answer efficiently with the help of an index built on the database. In most of our experiments, C$$_2$$ 2 O performs up to 2 orders of magnitude faster than all adapted algorithms in the literature. In particular, when the database size scales up to 100 million points, C$$_2$$ 2 O answers the 3D object retrieval within 10 s but all adapted exact algorithms need more than 1000 s.

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Fast 3D Scene Segmentation and Partial Object Retrieval Using Local Geometric Surface Features

Cited by 3 publications

References 29 publications

Robust Object Classification Approach Using Spherical Harmonics

Robust Object Classification Approach Using Spherical Harmonics

Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

On efficient 3D object retrieval

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