SHREC 15 track non rigid 3D shape retrieval

Date

2015-05-02

Author

Lıan, Zz
Zhang, Z
S, Choı
Elnaghy, H El
Sana, J El
Furuya, T
Gıachettı, A
Güler, Alp
Laı, L
Lı, C
Lı, H
Lımberger, Fa
Martın, R
Nakanıshı, Ru
Neto, Ap
Nonato, Lg
Ohbuchı, Ro
Pevzner, Kp
Pıckup, David
Rosın, Paul
Sharf, As
Sun, L
Sun, X
Tarı, Zehra Sibel
Ünal, Gözde
Wılson, Rc

Metadata

Show full item record

Item Usage Stats

236
views

0
downloads

Non-rigid 3D shape retrieval has become a research hotpot in communities of computer graphics, computer vision, pattern recognition, etc. In this paper, we present the results of the SHREC’15 Track: Non-rigid 3D Shape Retrieval. The aim of this track is to provide a fair and effective platform to evaluate and compare the performance of current non-rigid 3D shape retrieval methods developed by different research groups around the world. The database utilized in this track consists of 1200 3D watertight triangle meshes which are equally classified into 50 categories. All models in the same category are generated from an original 3D mesh by implementing various pose transformations. The retrieval performance of a method is evaluated using 6 commonly-used measures (i.e., PR-plot, NN, FT, ST, E-measure and DCG.). Totally, there are 37 submissions and 11 groups taking part in this track. Evaluation results and comparison analyses described in this paper not only show the bright future in researches of non-rigid 3D shape retrieval but also point out several promising research directions in this topic

URI

https://hdl.handle.net/11511/87042

DOI

https://doi.org/10.2312/3dor.20151064

Conference Name

8th Eurographics Workshop on 3D Object Retrieval

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

SHREC 16 Partial Matching of Deformable Shapes Cosmo, Luca; Rodola, Emanuele; Bronstein, Michael; Torsello, Andrea; Cremers, Daniel; Sahillioğlu, Yusuf (null; 2016-06-15) Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching pr...
CoSPAIR: Colored Histograms of Spatial Concentric Surflet-Pairs for 3D object recognition Logoglu, K. Berker; Kalkan, Sinan; Temizel, Alptekin (2016-01-01) Introduction of RGB-D sensors together with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this paper, two spatially enhanced local 3D descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surflet-Pairs (SPAIR) and Colored SPAIR (CoSPAIR). The proposed descriptors ar...
Automated learning rate search using batch-level cross-validation Kabakcı, Duygu; Akbaş, Emre; Department of Computer Engineering (2019) Deep convolutional neural networks are being widely used in computer vision tasks, such as object recognition and detection, image segmentation and face recognition, with a variety of architectures. Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given a specific network model and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the tr...
Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model Şahin, Uğur Berk; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2022-8) The success of deep learning in computer vision has sparked great interest in investigating deep learning-based algorithms also in many image processing applications, including image compression. The most popular end-to-end learned image compression approaches are based on auto-encoder architectures, where the image is mapped via convolutional neural networks (CNNs) into a transform (latent) representation that is quantized and processed again with CNNs to obtain the reconstructed image. The quantized laten...
Human motion analysis via axis based representations Erdem, Sezen; Tarı, Zehra Sibel; Department of Computer Engineering (2007) Visual analysis of human motion is one of the active research areas in computer vision. The trend shifts from computing motion fields to understanding actions. In this thesis, an action coding scheme based on trajectories of the features calculated with respect to a part based coordinate system is presented. The part based coordinate system is formed using an axis based representation. The features are extracted from images segmented in the form of silhouettes. We present some preliminary experiments that d...

Citation Formats

Z. Lıan et al., “SHREC 15 track non rigid 3D shape retrieval,” Zurich, Switzerland, 2015, p. 107, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87042.