Dorofeev K. A.

Kalschikov V. V.

Ruchay A. N.

Ruchay A. N., Dorofeev K. A., Kalschikov V. V.

English

Samara University

Accuracy analysis of 3D object reconstruction usingmesh filteringA.N. Ruchay1,2, K.A. Dorofeev2, V.V. Kalschikov21Federal Research Centre of Biological Systems and Agro-technologies of the Russian Academy of Sciences, 9 Yanvarya street 29, Orenburg, Russia, 4600002Chelyabinsk State University, Bratiev Kashirinykh street 129, Chelyabinsk, Russia, 454001Abstract. In this paper, we analyze the accuracy of 3D object reconstruction using mesh filtering applied to data from a RGB-D sensor. Various methods of mesh filtering are tested and compared with respect to the reconstruction accuracy using real data. In order to improve the accuracy of 3D object reconstruction, an efficient method of mesh filtering is designed. The resented results show an improvement in the accuracy of 3D object reconstruction using the proposed mesh filtering algorithm.1. IntroductionThe availability of high-quality 3D mesh models of real objects has become an essentialprerequisite in various applications, such as medicine, agriculture, architecture, engineering,industrial metrology, cultural heritage preservation and restoration [1, 2, 3]. Accurate 3Dobject reconstruction is an important aspect for object recognition, object retrieval, sceneunderstanding, object tracking, virtual maintenance and visualization [4, 5, 6].Mesh denoising is always an inevitable stage to eliminate or to reduce these negative impactsfor processing, because of the limitation in the accuracy of scanning devices, the influence ofsurrounding environment, and the acquired raw data contains noise.Noise and holes can greatly affect to the accurate of 3D reconstruction [7, 8, 9], therefore,noise-reduction and hole-filling enhancement algorithms are intended to serve as preprocessingstep for 3D reconstruction systems with Kinect cameras [10, 11, 12]. To reduce impulsive noiseand to fill small holes, filters [13, 14, 15, 16, 17] can be used.In this paper, we are interested in the design of a filtering algorithm of a mesh to improvethe quality of 3D reconstruction. The existing approaches can be classified in two core groupsthat treat differently noise and salient features, known as isotropic and anisotropic, respectively.In common approach of noise reduction it is supposed that the raw mesh contains the groundtruth, distorted by noise such as additive one. Although this approach could be used forquantitative comparison (with PSNR, MSE, etc), common methods reduce only artificial noisebut not original noise contained in the raw mesh. In this paper, we consider denoising meshalgorithms for 3D object reconstruction. We propose a denoising method using a mesh as theinput. We also evaluate the performance of denoising methods with respect to the accuracyof 3D object reconstruction. Actually, we calculate Hausdorf distance between point cloud ofСекция: Обработка изображений и дистанционное зондирование Земли VI Международная конференция и молодёжная школа «Информационные технологии и нанотехнологии» (ИТНТ-2020)the accurate laser model and point cloud from mesh, and cloud to mesh distance between pointcloud of accurate laser model and filtered meshes.General denoising methods are not designed to clean coarse noise contained in the inputmesh. Therefore, our main goal is to evaluate denoising methods in terms of the reconstructionaccuracy which depends on the quality of the input mesh.The performance of the proposed algorithm is compared in terms of the accuracy of 3Dobject reconstruction and speed with the following depth denoising algorithms: Robust andHigh Fidelity Mesh Denoising (RHFMD) [18], Static/Dynamic Filtering for Mesh Geometry(SDFMG) [19], Guided mesh normal filtering (GMN) [20], Bilateral mesh denoising (BMD) [21],Non-iterative, feature-preserving mesh smoothing (NIFPMS) [22], Fast and Effective Feature-Preserving Mesh Denoising (FEFPMD) [23], Bilateral Normal Filtering for Mesh Denoising(BNFMD) [24], Mesh denoising via L0 minimization (MDLM) [25].The paper is organized as follows. Computer simulation results are provided in Section 2.Finally, Section 3 summarizes our conclusions.2. Experimental resultsIn this section, we evaluate the performance of the tested mesh denoising methods in terms ofreconstruction accuracy which depends on the quality of the input mesh.Extensive experiments revealed that very good denoising results cannot be achieved usingthe following filters: RHFMD [18], GMN [20], BMD [21], FEFPMD [23], MDLM [25]. The mainreason of this is uncorrected mesh after filtering, therefore, we do not use these filters for ournext experiments and comparisons.We compare the following mesh denoising algorithms in terms of the accuracy of 3D objectreconstruction: SDFMG [19], NIFPMS [22], BNFMD [24]. The experiments are carried out ona PC with Intel(R) Core(TM) i7-4790 CPU @ 3.60 GHz and 16 GB memory.In our experiments we use the meshes of a lion, an apollo, and an anatomy from dataset [26].We construct couples of meshes for each model using the following steps:(i) Registration RGB and depth data.(ii) Making point clouds.(iii) Making input meshes from point clouds.(iv) Calculating Hausdorf distance (HD) between point cloud of accurate laser model and pointcloud of filtered mesh, and cloud to mesh distance (CMD) between point cloud of accuratelaser model and filtered meshes.The corresponding calculated HD and CMD metrics for the model a lion, an apollo, andan anatomy without filtering (HDW and CMDW) and after denoising (HDF and CMDF) bySDFMG, NIFPMS, BNFMD filters are shown in Table 1. The NIFPMS filter yields the bestresult in terms HD and CMD evaluation among all mesh denoising algorithms.3. ConclusionIn this paper, we compared various mesh denoising algorithms in terms of the accuracy of 3Dobject reconstruction using real data from a RGB-D sensor. The experiment has shown thatthe NIFPMS filter yields the best result in terms of the accuracy of 3D object reconstructionamong all mesh denoising algorithms.4. AcknowledgmentsThis work was supported by the Russian Science Foundation, grant no. 17-76-20045.Секция: Обработка изображений и дистанционное зондирование Земли Accuracy analysis of 3D object reconstruction using mesh filteringVI Международная конференция и молодёжная школа «Информационные технологии и нанотехнологии» (ИТНТ-2020) 740Table 1. Calculated metrics between two point clouds of each model without filtering.Object Filters HDW HDF CMDW CMDFAnatomy SDFMG 0.041397 0.041446 0.005053 0.005055NIFPMS 0.041397 0.041497 0.005053 0.005147BNFMD 0.041397 0.041413 0.005053 0.005051Apollo SDFMG 0.005057 0.004935 0.000225 0.000266NIFPMS 0.005057 0.005091 0.000225 0.000118BNFMD 0.005057 0.005073 0.000225 0.000289Lion SDFMG 0.004753 0.004879 0.001281 0.001238NIFPMS 0.004753 0.004841 0.001281 0.001393BNFMD 0.004753 0.004838 0.001281 0.0012845. References[1] Ruchay, A. 3d object reconstruction using multiple kinect sensors and initial estimation ofsensor parameters / A. Ruchay, K. Dorofeev, A. Kober // Proc. SPIE. – 2018. – Vol. 10752. –P. 1075222.[2] Ruchay, A. Fusion of information from multiple kinect sensors for 3d objectreconstruction / A. Ruchay, K. Dorofeev, V. Kolpakov // Computer Optics. – 2018. – Vol.42(5). – P. 898-903. DOI: 10.18287/2412-6179-2018-42-5-898-903.[3] Ruchay, A.N. 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Accuracy analysis of 3D object reconstruction using mesh filtering

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Accuracy analysis of 3D object reconstruction using mesh filtering

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