| Year | Track Title | Organizers | Reference | Database Size | Downloads | Data Type & Acquisition | Category / Task |
|---|---|---|---|---|---|---|---|
| 2006 | 3D Shape Retrieval Contest | Remco C. Veltkamp, Remco Ruijsenaars, Michela Spagnuolo, Roelof van Zwol, Frank ter Haar | Veltkamp et al. (2006) | 1,814 models (30 queries) |
— | 1,814 models with 3 relevance scores (0-2). Acquired from the Princeton Shape Benchmark. | Generic Classification |
| 2007 | Watertight Models | Daniela Giorgi, Silvia Biasotti, Laura Paraboschi daniela.giorgi@ge.imati.cnr.it silvia.biasotti@ge.imati.cnr.it |
Giorgi et al. (2007), in Veltkamp and ter Haar (2007) | 400 watertight mesh models | Unavailable | 400 models divided into 20 classes. Scraped from Drexel, AIM@SHAPE, PSB, CAESAR, McGill, INRIA, etc. Each model is queryied against the rest of the database. Ground truth was established manually. | Watertight Retrieval |
| 2007 | CAD Models | Yagnanarayanan Kalyanaraman, Karthik Ramani shrec@purdue.edu |
Kalyanaraman & Ramani (2007) in Veltkamp and ter Haar (2007) | — | Unavailable | Triangulated meshes of CAD parts in vendor-neutral formats. Sourced from Purdue Engineering Shape Benchmark. The query set was developed for testing search scenarios in: subdivided/decimated models, parmetric variation in the model, slightly modified models and partial shape. | CAD Retrieval |
| 2007 | Partial Matching | Simone Marini, Laura Paraboschi, Silvia Biasotti temerina@informatik.uni-freiburg.de |
Marini et al. (2007) in Veltkamp and ter Haar (2007) | 400 target models 30 partial queries |
Unavailable | Watertight meshes (.off format). Queries generated by cutting, scaling, and rotating target models. | Partial Retrieval |
| 2007 | Protein Retrieval | Maja Temerinac, Marco Reisert, Hans Burkhardt temerina@informatik.uni-freiburg.de |
Temerinac et al. (2007) in Veltkamp and ter Haar (2007) | 685 proteins (27 SCOP folds) |
Data (15.5 MB) | 685 protein domains divided into 27 folds. Data sourced from SCOP database. | Protein Classification |
| 2007 | 3D Face Models | Frank B. ter Haar, Remco C. Veltkamp |
Frank ter Haar, Remco C. Veltkamp (2007) in Veltkamp and ter Haar (2007) | 1,512 face models | Unavailable | Emotionless face models generated using a morphable face model (75k vertices, 150k faces). | Face Retrieval |
| 2008 | Stability on Watertight Models | Silvia Biasotti, Marco Attene silvia.biasotti@ge.imati.cnr.it marco.attene@ge.imati.cnr.it |
Biasotti & Attene (2008) | Set A: 1,229 models Set B: 1,500 models |
Unavailable | Perturbed watertight models (Gaussian noise, topology noise, sampling pattern variations). | Watertight Retrieval |
| 2008 | Classification of Watertight Models | Daniela Giorgi, Simone Marini daniela@ge.imati.cnr.it simone@ge.imati.cnr.it |
Giorgi and Marini (2008) | 570 training models 76 queries |
Unavailable | 3 class levels (coarse: 12, intermediate: 39, fine: 109). Models sourced from SHREC '07, AIM@SHAPE, Utrecht, etc. | Watertight Classification |
| 2008 | CAD Models Track | Ramanathan Muthuganapathy, Karthik Ramani rmuthuga@purdue.edu ramani@purdue.edu |
Muthuganapathy & Ramani (2008) | 45 query models | Dataset (66MB) Query (4MB) |
CAD parts in vendor-neutral formats organized in 3 super-classes and 45 sub-classes from Purdue Benchmark. | CAD Retrieval |
| 2008 | Generic Models | Ryutarou Ohbuchi ohbuchi@yamanashi.ac.jp |
Ohbuchi et al. (2008) [pdf] |
1,814 models (2 sets of 30 queries) |
Unavailable | Test/Train sets of 907 models each from the Princeton Shape Benchmark. | Generic Retrieval |
| 2008 | 3D Face Scans | Frank B. ter Haar, Mohamed Daoudi, Remco C. Veltkamp fhaar@cs.uu.nl mohamed.daoudi@telecom-lille1.eu |
ter Haar et al. (2008) [pdf] |
427 range scans (61 subjects) |
Unavailable | 7 scans per subject with varying facial expressions from the GavabDB laser range dataset. | Face Retrieval |
| 2009 | Structural Shape Retrieval of Watertight Models | J. Hartveldt, M. Spagnuolo jhartvel@cs.uu.nl michi@ge.imati.cnr.it |
Godil et al. (2009) | 200 models (10 classes) |
OFF format (183 MB) | 200 models in 10 main classes, each having 2 subclasses with 10 models each. | Watertight Retrieval |
| 2009 | Querying with Partial Models | A. Godil, H. Dutagaci afzal.godil@nist.gov |
Dutagaci et al. (2009) | 720 target models in 40 classes, 40 queries | Data (188.4 MB) | 720 target models from NIST benchmark. Queries contain 20 partial cut models and 20 range scans from NextEngine scanner. | Partial Retrieval |
| 2009 | Generic Shape Retrieval | A. Godil, H. Dutagaci afzal.godil@nist.gov |
Godil et al. (2009) | 720 data objects 80 query objects |
Data (131.3 MB) | 800 3D objects classified into 40 categories in ASCII .off format. Sourced from NIST Generic Shape Benchmark. | Generic Retrieval |
| 2010 | Large Scale Retrieval | Remco C. Veltkamp, Geert-Jan Giezeman G.J.Giezeman@uu.nl |
Lian et al. (2010) | 10,000 shapes (.ply) 40 queries |
Unavailable | Dataset contains 493 real models and 9,507 randomly generated procedural shapes without color/texture. | Large Scale Retrieval |
| 2010 | Robust Shape Retrieval | A. M. Bronstein, M. M. Bronstein, U. Castellani, L. J. Guibas, M. Ovsjanikov mbron@cs.technion.ac.il |
Bronstein et al. (2010) | 1,171 test shapes 624 train shapes |
Unavailable | Transformed triangular meshes (isometries, noise, topology changes). Data from TOSCA, Sumner, and PSB collections. | Transformations Retrieval |
| 2010 | Feature Detection and Description | A. M. Bronstein et al. mbron@cs.technion.ac.il |
Bronstein et al. (2010) | 138 meshes (3 null meshes) |
Unavailable | 3 null shapes subjected to 45 transformation variations (scaling, holes, noise, downsampling). TOSCA dataset. | Feature Detection Detection |
| 2010 | Correspondence Finding | A. M. Bronstein et al. mbron@cs.technion.ac.il |
Bronstein et al. (2010) | 138 meshes (3 null meshes) |
Unavailable | Evaluates correspondence finding between modified mesh shapes and original null shapes. TOSCA dataset. | Transformations Correspondence |
| 2010 | Generic 3D Warehouse | T. P. Vanamali, A. Godil, H. Dutagaci afzal.godil@nist.gov |
Vanamali et al. (2010) | 3,168 models | Data (358.6 MB) | 3,168 SketchUp (.skp) models across 43 categories, web-crawled from Google 3D Warehouse. | Generic Retrieval |
| 2010 | Non-rigid 3D Shape Retrieval | Z. Lian, A. Godil afzal.godil@nist.gov |
Lian & Godil (2010) | 200 models | Data (259.1 MB) | 200 watertight meshes in 10 categories (ants, humans, snakes, etc.) modified from McGill Articulated Benchmark. | Non-Rigid Retrieval |
| 2010 | Range Scan Retrieval | H. Dutagaci, A. Godil, C. P. Cheung afzal.godil@nist.gov |
Dutagaci et al. (2010) | 800 target models 117 range scans |
Data (473.3 MB) | 800 complete 3D models (.off) target set; queries captured from 40 real objects using a Minolta Laser Scanner. | Range Scans Retrieval |
| 2010 | Protein Model Classification | L. Mavridis, V. Venkatraman, D. W. Ritchie |
Mavridis et al. (2010) | 1,000 proteins 50 queries |
Unavailable | 1,000 proteins from 100 CATH superfamilies with sequence data masked to enforce purely structural evaluation. | Protein Classification |
| 2011 | Generic Shape Retrieval | Helin Dutagaci, Afzal A. Godil afzal.godil@nist.gov |
Dutagaci et al. (2011) | 1,000 models (50 classes) |
Unavailable | 1,000 models (.off format) with 20 per class. Sourced from NIST Generic Benchmark and Generic 3D Warehouse. | Generic Retrieval |
| 2011 | Robust Feature Detection Benchmark | A. M. Bronstein, M. M. Bronstein, A. Kovnatsky, R. Litman, A. Zaharescu mbron@cs.technion.ac.il |
Boyer et al. (2011) | 56 meshes | Unavailable | One human null mesh transformed under 11 transformation classes and 5 strength levels. TOSCA dataset. | Feature Detection Detection |
| 2011 | Non-rigid 3D Watertight Meshes | Zhouhui Lian, Afzal Godil shrec@nist.gov |
Lian et al. (2011) | 600 triangle meshes | Data (259.1 MB) | 600 watertight models split across 30 categories. 30 original models deformed 19 times using skeleton rigs. | Non-Rigid Retrieval |
| 2011 | 3D Face Models Retrieval | Stefan van Jole, Remco C. Veltkamp, Mohamed Daoudi, Ben Amor Boulbaba mohamed.daoudi@telecom-lille1.eu Remco.Veltkamp@cs.uu.nl |
Veltkamp et al. (2011) | 780 scans (130 masks) |
Unavailable | 780 scans collected from 130 physical masks using Roland and Escan 3D scanner systems. | Face Retrieval |
| 2012 | 3D Mesh Segmentation | Guillaume Lavoué et al. |
Lavoué et al. (2012) | 28 models 112 ground-truths |
Unavailable | 28 watertight manifold triangle meshes across 5 classes with 4 manual segmentations per mesh from 36 human volunteers. | Segmentation Retrieval |
| 2012 | Sketch-Based 3D Shape Retrieval | Afzal A. Godil afzal.godil@nist.gov |
Li et al. (2012) | 400 3D models 262 sketches |
Data (117.9 MB) | Target set of 400 watertight models (WMB dataset); 250 hand-drawn sketches and 12 standard line drawings. | Sketch Retrieval |
| 2012 | Generic 3D Shape Retrieval | Afzal A. Godil afzal.godil@nist.gov |
Li et al. (2012) | 1,200 meshes (60 classes) |
Data (133.6 MB) | 1,200 triangle meshes (20 per class) compiled from previous SHREC generic datasets, WMB, and PSB. | Generic Retrieval |
| 2012 | Stability on Abstract Shapes | Silvia Biasotti et al. |
Biasotti et al. (2012) | 504 meshes | — | 504 watertight meshes synthesized from 18 mathematical primitive shapes perturbed with 9 transformation types. | Transformations Classification |
| 2013 | Large-Scale Partial Shape Retrieval | I. Sipiran, R. Meruane, B. Bustos, T. Schreck iasipiranm@gmail.com |
Sipiran et al. (2013) | 360 target models 7,200 queries |
Data (800 MB) | 360 target shapes from SHREC '09. 7,200 queries generated by simulating range scans via icosahedral projection planes. | Range Scans Retrieval |
| 2013 | Low-Cost Depth-Sensing Cameras | Joao Machado, Alfredo Ferreira alfredo.ferreira@ist.utl.pt joaoprmachado@gmail.com |
Machado et al. (2013) | 192 target models 12 query models |
Unavailable | Low-fidelity watertight meshes scanned using Microsoft Kinect sensors and ReconstructMe software. | Range Scans Retrieval |
| 2013 | Retrieval of Textured 3D Models | Andrea Cerri, Silvia Biasotti andrea.cerri@ge.imati.cnr.it silvia.biasotti@ge.imati.cnr.it |
Cerri et al. (2013) | 240 watertight meshes (10 classes) |
Unavailable | Textured watertight meshes (6 base models per class with 3 textures each), modified with 4 transformation types. | Textured Retrieval |
| 2013 | Large Scale Sketch-Based Retrieval | Bo Li, Yijuan Lu, Afzal Godil, Tobias Schreck sketch@nist.gov li.bo.ntu0@gmail.com |
Li et al. (2013) | 1,258 3D models 7,200 sketch queries |
Data (602.9 MB) | 7,200 human sketches (Eitz dataset) and 1,258 3D models from PSB spanning 90 categories. | Sketch Retrieval |
| 2014 | Automatic Location of Landmarks used in Manual Anthropometry | A. Giachetti, E. Mazzi, F. Piscitelli andrea.giachetti@univr.it |
Godil & Li (2014) [pdf] |
50 train / 50 test | Train/Test (98.6 MB) | Human body models annotated with manual landmark locations (e.g., Acromiale, Radiale, Stylion). | Landmark Retrieval |
| 2014 | Shape Retrieval of Non-Rigid 3D Human Models | David Pickup, Xianfang Sun, Paul L Rosin, Ralph R Martin, Zhouhui Lian, Zhiquan Cheng d.pickup(at)cs.cf.ac.uk |
Pickup et al. (2024) [pdf] |
400 (real), 300 (synthetic) | - | The real dataset is from point-clouds contained within the Civilian American and European Surface Anthropometry Resource (CAESAR), synthetic dataset created in DAZ Studio | Non-rigid Retrieval |
| 2014 | Retrieval and classification on Textured 3D Models | Andrea Cerri, Silvia Biasotti andrea.cerri(at)ge.imati.cnr.it and silvia.biasotti(at)ge.imati.cnr.it |
Biasotti et al. (2014) [pdf] |
572 watertight mesh models, 16 geometric classes | - | Synthetic textured mesh models | Textured Retrieval |
| 2014 | Extended Large Scale Sketch-Based 3D Shape Retrieval | Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck sketch(at)nist.gov and b_l58(at)txstate.edu |
Li et al. (2014) [pdf] |
8987 3D models in 171 classes, 12680 sketches | SHREC2014_SBR.zip (1.03 GB) | Generic, articulated, CAD, architecture models, human-drawn sketches | Sketch Retrieval |
| 2014 | Large Scale Comprehensive 3D Shape Retrieval | Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck Generic3D(at)nist.gov and b_l58(at)txstate.edu |
Li et al. (2014) [pdf] |
8987 3D models, 171 classes | Data (877.5 MB) | 8,987 triangle meshes, manual classification | Large scale Retrieval |
| 2015 | Canonical Forms for Non-Rigid 3D Shape Retrieval | David Pickup, Xianfang Sun, Paul L. Rosin, Ralph R. Martin, Zhiquan Cheng pickupd@cardiff.ac.uk |
Pickup et al. (2015) [pdf] |
100 train, 100 test | Data (81.5MB) | Canonical forms to factor out a shape’s pose, for rigid retrieval systems to retrieve non-rigid shapes/td> | Non-rigid Retrieval |
| 2015 | Non-rigid 3D Shape Retrieval | Zhouhui Lian lianzhouhui@pku.edu.cn |
Lian et al. (2015) [pdf] |
1200 watertight triangle meshes, 50 categories | Data (241 MB) | Models in same category generated by transforming original mesh | Non-Rigid Retrieval |
| 2015 | Scalability of Non-Rigid 3D Shape Retrieval | I. Sipiran sipiran@dbvis.inf.uni-konstanz.de |
Sipiran et al. (2015) [pdf] |
96487 models, 9 classes | - | Similated range images | Non-rigid Retrieval |
| 2015 | 3D Object Retrieval with Multimodal Views | Yue Gao kevin.gaoy@gmail.com |
Gao et al. (2025) [pdf] |
505 objects, 311 queries, 73 images+depth / object | - | Recorded by Kinect sensors from 3 directions | Textured/Multiview Retrieval |
| 2015 | Retrieval of non-rigid (textured) shapes using low quality 3D models | Andrea Giachetti, Francesco Fornasa, Francesco Farina andrea.giachetti@univr.it |
Giachetti et al. (2015) [pdf] |
240 textured, 120 non-textured of 12 toys | Data (33.4MB) | Point cloud meshed to create the watertight models, scanned with Asus Xtion Live Pro for depth and texture | Non-rigid/Textured Retrieval |
| 2015 | Retrieval of Objects Captured with Kinect One Camera | Pedro B. Pascoal, Pedro Proença, Miguel Sales Dias, Alfredo Ferreira t-pedrop@microsoft.com |
Pascoal et al. (2015) [pdf] |
175 objects in 18 classes | - | Household objects with Kinect 1 | Range scans Retrieval |
| 2015 | Range Scans based 3D Shape Retrieval | A. Godil afzal.godil@nist.gov |
Godil et al. (2015) [pdf] |
1200 target, 180 query | Data (655.4 MB) | 60 object, Minolta scanner | Range scans Retrieval |
| 2016 | Retrieval of Human Subjects from Depth Sensor Data | Andrea Giachetti andrea.giachetti@univr.it |
Giachetti et al. (2016) [pdf] |
720 testing, 180 training | Train (93 MB), test (319 MB) | 18 scans for each subject, 40 test subjects, 10 training subjects | Range scans (human) Retrieval |
| 2016 | 3D Sketch-Based 3D Shape Retrieval | Bo Li, Yijuan Lu li.bo.ntu0@gmail.com |
Li et al. (2016) [pdf] |
300 3D sketches in 30 classes, 1258 models in 90 classes | Data (71 MB) | 3D sketches where created with a Kinect | Sketch Classification |
| 2016 | Matching of Deformable Shapes with Topological Noise | Zorah Lähner, Emanuele Rodolà, Michael Bronstein, Daniel Cremers laehner@in.tum.de |
Lähner et al. (2016) [pdf] |
25 shapes (15 for training 10 for testing) | - | The (human) shapes are deformable and have topological noise, created with DAZ 3D studio | Non-rigid Retrieval |
| 2016 | Partial Matching of Deformable Shapes | Luca Cosmo, Emanuele Rodolà, Michael Bronstein, Andrea Torsello shrec2016@dais.unive.it |
Cosmo et al. (2016) [pdf] |
76 base shapes in 8 classes, 596 total shapes | - | Remeshed to 10K vertices, made partial either by adding holes randomly (276 shapes) or cutting the shape in a random direction (320 shapes), base shapes are from the TOSCA dataset | Partial Retrieval |
| 2016 | Shape Retrieval of Low-Cost RGB-D Captures | Pedro Pascoal, Pedro Proença, Miguel Sales Dias, Alfredo Ferreira pmbp@tecnico.ulisboa.pt |
Pascoal et al. (2016) [pdf] |
200 models | - | 90 frame pairs of RGB and Depth images, models from the 3D SketchUp Warehouse | Range scans Classification |
| 2016 | Partial Shape Queries for 3D Object Retrieval | Ioannis Pratikakis, Michalis Savelonas, Fotis Arnaoutoglou, Anestis Koutsoudis, Theoharis Theoharis ipratika@ee.duth.gr |
Pratikakis et al. (2016) [pdf] |
383 models in 6 classes | - | 3 qualities: artificial (slicing and capfilling), real queries high quality (smartSCAN), real queries low quallity (Kinect) | Partial Retrieval |
| 2016 | Large-Scale 3D Shape Retrieval from ShapeNet Core55 | Manolis Savva, Fisher Yu, Hao Su shrec2016shapenet@gmail.com |
Savva et al. (2016) [pdf] |
51190 3D models, 70/10/20% training / validation / test | - | Categorized into 55 WordNet categories and 204 sub-categories, models where deduplicated | Scalability Retrieval |
| 2016 | 3D Object Retrieval with Multimodal Views | Yue Gao, Anan Liu, Weizhi Nie, Qionghai Dai weizhinie@tju.edu.cn |
Gao et al. (2016) [pdf] |
605 objects, 200 queries, 405 test/target in 60 categories | Download (size) | 100 3D printed and 505 real 3D objects scanned with Kinect from 3 angles | Range scans/Multiview Retrieval |
| 2017 | RGB-D to CAD Retrieval with ObjectNN Dataset | Binh-Son Hua, Quang-Hieu Pham, Minh-Khoi Tran, Quang-Trung Truong | Hua et al. (2017) [pdf] |
4975 models, 50 / 25 / 25 train / validation / test | Data (3,8 GB), extras on Github | 1667 SceneNN objects, 3308 ShapeNet models into 20 categories | CAD Retrieval |
| 2017 | 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset | Quentin De Smedt, Hazem Wannous, Jean-Phillipe Vandeborre david.filliat@ensta-paristech.fr |
De Smedt et al. (2017) [pdf] |
2800 sequences, 14 gestures | Data (5,8 GB) | sequences of 14 hand gestures performed multiple times with hand or finger | Gesture Classification |
| 2017 | Large-Scale 3D Shape Retrieval from ShapeNet Core55 | Manolis Savva, Hao Su, Fisher Yu, Thomas Funkhouser shrecshapenet@gmail.com |
Savva et al. (2017) [pdf] |
51162 3D models in 55 WordNet categories, 204 sub-categories | Data (50 GB) | models where deduplicated | Scalability Retrieval |
| 2017 | Protein Shape Retrieval | Haiguang Liu | Song et al. (2017) [pdf] |
5854 target, 10 queries | - | low quality models removed from original set | Protein Retrieval |
| 2017 | Point-Cloud Shape Retrieval of Non-Rigid Toys | Frederico A. Limberger, Richard C. Wilson pronto-group@york.ac.uk |
Limberger et al. (2017) [pdf] |
100 3D scanned models, 10 classes | Data (3,6 MB) | 10 poses per model, scanned by Head & Face Color 3D Scanner | Range scan Classification |
| 2017 | Deformable Shape Retrieval with Missing Parts | Emanuele Rodolà, Or Litany, Michael Bronstein | Rodolà et al. (2017) [pdf] |
1216 train (holes), 1078 test (holes), 1082 train (range), 882 test (range) | - | Non-rigid deformations, different amounts and types of partiality, topological changes induced by mesh gluing in areas of contact | Partial Retrieval |
| 2017 | Retrieval of surfaces with similar relief patterns | Silvia Biasotti, Andrea Giachetti andrea.giachetti@univr.it |
Biasotti et al. (2017) [pdf] |
720 models in 15 classes | - | 180 models + 3 tesselation variations of each model | Relief Classification |
| 2018 | 2D Sketch-Based 3D Scene Retrieval | Juefei Yuan, Bo Li, Yijuan Lu bo.li@usm.edu |
Yuan et al. (2018) [pdf] |
250 Scene sketches, 1000 3D scene models in 10 classes | Data (4,5 GB) | 180 train sketches, 70 test sketches, 700 train 300 test 3d models, 3D models from Google 3D warehouse | Sketch Retrieval |
| 2018 | 2D Image-Based 3D Scene Retrieval | Hameed Abdul-Rashid, Juefei Yuan, Bo Li, Yijuan Lu bo.li@usm.edu |
Abdul-Rashid et al. (2018) [pdf] |
10.000 2D images, 1000 3D models in 10 classes | Data (5,6 GB) | 180 train images, 70 test images, 700 train, 300 test 3d models, 3D models from Google 3D warehouse | Image Retrieval |
| 2018 | RGB-D Object-to-CAD Retrieval | Quang-Hieu Pham, Binh-Son Hua quanghieu_pham@mymail.sutd.edu.sg |
Pham et al. (2018) [pdf] |
2101 query 3308 target | - | Real-world SceneNN and ScanNet target dataset, subset of ShapeNetSem | CAD Retrieval |
| 2018 | Protein Shape Retrieval | Matthieu Montes, Florent Langenfeld matthieu.montes@cnam.fr |
Langenfeld et al. (2018) [pdf] |
2267 protein structures in 107 classes | Data (3,1 GB) | Use each member of class as query for the rest of the class | Protein Retrieval |
| 2018 | Retrieval of grey patterns depicted on 3D models | E. Moscoso Thompson, S. Biasotti elia.moscoso@ge.imati.cnr.it, silvia.biasotti@ge.imati.cnr.it |
Moscoso Thompson et al. (2018) [pdf] |
300 surfaces 20 base models | sample (10 MB) | patterns applied to the meshes of basic shapes (cube, cylinder, cup, vase) | Textured Classification |
| 2018 | Recognition of geometric patterns over 3D models | E. Moscoso Thompson, S. Biasotti, G. Sorrentino, M. Polig, S. Hermon elia.moscoso@ge.imati.cnr.it, silvia.biasotti@ge.imati.cnr.it |
Biasotti et al. (2018) [pdf] |
8 query meshes, 6 patterns 30 models | - | 25 models have at least 1 pattern, archeological artefacts from EU H2020 project GRAVITATE | Relief Retrieval |
| 2019 | Extended 2D Scene Sketch-Based 3D Scene Retrieval | Juefei Yuan, Hameed Abdul-Rashid, Bo Li, Yijuan Lu, Tobias Schreck juefei.yuan@usm.edu |
Yuan et al. (2019) [pdf] |
750 2D scene sketches, 3000 3D scene models in 30 classes | Data (16,3 GB) | per class 18 train and 7 test sketches, 70 train and 30 test models | Sketch Retrieval |
| 2019 | Extended 2D Scene Image-Based 3D Scene Retrieval | Hameed Abdul-Rashid, Juefei Yuan, Bo Li, Yijuan Lu, Tobias Schreck hameed.abdulrashid@usm.edu |
Abdul-Rashid et al. (2019) [pdf] |
30000 2D, 3000 3D in 30 classes | Data (16,3 GB) | Images from ImageNet, models from 3D Warehouse | Image Retrieval |
| 2019 | Feature Curve Extraction on Triangle Meshes | E. Moscoso Thompson elia.moscoso@ge.imati.cnr.it |
Moscoso Thompson et al. (2019) [pdf] |
15 surfaces | Samples (4,1 MB) | Models scanned or made in silico, derived from Turbosquid repository of 3D models or the Visionair shape workbench | Detection Retrieval |
| 2019 | Protein Shape Retrieval | Florent Langenfeld, Matthieu Montes matthieu.montes@cnam.fr |
Langenfeld et al. (2019) [pdf] |
5298 modelsin 17 classes | Species proteins Off-files (11.6 GB) | From the SCOPe database, then randomly selected to decrease to 5298 models | Protein Retrieval |
| 2019 | Classification in Cryo-Electron Tomograms | lja Gubins, Gijs van der Schot, Remco C. Veltkamp, Friedrich G. Forster i.gubins@uu.nl |
Gubins et al. (2019) [pdf] |
10 tomograms, 2540 proteins, 12 classes | Data (6,5 GB) | 10 reconstructed tomograms obtained from simulated cell-like volume, each filled with on average 2500 non-overlapping proteins. | Protein Classification |
| 2019 | Online Gesture Recognition | Fabio Marco Caputo, Andrea Giachetti fabiomarco.caputo@univr.it |
Caputo et al. (2019) [pdf] |
training set of 60 recordings from 4 subjects, test set of 135 recordings, 5 gestures | Train (8.9 MB) | Recordings done in VR Oculus Rift in interactive setting | Gesture Classification |
| 2019 | Monocular Image-based 3D Model Retrieval | An-An Liu, Wei-Zhi Nie, Wen-Hui Li, Dan Song, Yu-Qian Li, He-Yu Zhou, Shu Xiang, Wei-Jie Wang liwenhui@tju.edu.cn |
Li et al. (2019) [pdf] |
21000 2D images, 7690 3D objects, 12 views/object, 21 classes | - | 2D images collected from ImageNet, 3D objects collected from NTU, PSB, ModelNet40, ShapeNet | Image-based Retrieval |
| 2019 | Shape Correspondence with Isometric and Non-Isometric Deformations | Roberto Dyke, Caleb Stride, Yu-Kun Lai, and Paul L. Rosin DykeRM@cardiff.ac.uk |
Dyke et al. (2019) [pdf] |
76 shape pairs | Data (53.1 MB) | Artec3D Space Spider scanner, articulating, bending, stretching, topological deformations | Transformations Correspondence |
| 2019 | Correspondence in Humans with Different Connectivity | Simone Melzi, Riccardo Marin, Emanuele Rodolà , Umberto Castellani simone.melzi@univr.it |
Melzi et al. (2019) [pdf] |
430 shape pairs | Data (48.9 MB) | Models from SMPL, FAUST, SCAPE, TOSCA, SPRING, MoSh Mocap, Princeton, CEASAR, SHREC14 Non-Rigid 3D Human, and others | Humans Correspondence |
| 2020 | Non-rigid Shape Correspondence of Physically-Based Deformations | Roberto Dyke, Feng Zhou, Yu-Kun Lai, and Paul L. Rosin DykeRM@cardiff.ac.uk |
Dyke et al. (2020) [pdf] |
11 partial scans and 1 full scan of rabit | Data (13 MB) | Artec3D Space Spider scans of soft stretchy toy rabbit, with stretch, indent, twist, inflate deformations | Transformation Correspondence |
| 2020 | Shape correspondence with non-isometric deformations | Roberto Dyke, Yu-Kun Lai, and Paul L. Rosin DykeRM@cardiff.ac.uk |
Dyke et al. (2020) | 14 animal models | Data (10 MB) | Meshes (simplified to) 100.000 traingles | Transformation Correspondence |
| 2020 | 3D point cloud semantic segmentation for street scenes | Tao Ku, Remco C. Veltkamp t.ku@uu.nl |
Ku et al. (2020) | 60 training, 20 test 3D point cloud for street scenes | Drive (2,6 GB) | Scenes from Cyclomedia Technology with panoramic camera and velodyne HDL-32 Lidar sensor, manually labelled, two million points per point cloud | Range scans Classification |
| 2020 | Multi-domain protein shape retrieval challenge | Matthieu Montès, Florent Langenfeld matthieu.montes@cnam.fr |
Langenfeld et al. (2020) | 588 protein chains from 26 species | Website (1.2 GB) | SCOPe database en PDB (Protein Data Bank), classes at least 10 members, duplicates removed | Protein Classification |
| 2020 | Retrieval of digital surfaces with similar geometric reliefs | Elia Moscoso Thompson, Silvia Biasotti, Andrea Giachetti elia.moscoso@ge.imati.cnr.it |
Moscoso Thompson et al. (2020) | 220 surfaces | Data (227 MB) | Relief patterns applied to randomly rotated models | Relief Classification |
| 2020 | Classification in cryo-electron tomograms | Ilja Gubins, Marten Chaillet, Gijs van der Schot, Remco Veltkamp, Friedrich Förster i.gubins@uu.nl |
Gubins et al. (2020) | 9 tomograms, between 2400 and 2800 proteins, 12 classes | Data (7,2 GB)) | Sphericity, radius, electron density maps generated | Protein Classification |
| 2020 | River gravel characterization | Andrea Giachetti, Silvia Biasotti, Luigi Fraccarollo, Filippo Andrea Fanni andrea.giachetti@univr.it |
Giachetti et al. (2020) [pdf] |
256 surfaces, 8 classes of grain | Data (420 MB) | Photogrammetry to create 3D models of the patches | Textured Classification |
| 2020 | 6D object pose estimation | Honglin Yuan, Remco C. Veltkamp h.yuan@uu.nl |
Yuan et al. (2020) [pdf] |
500 PNG 1280x720 image pairs, 400 trainig, 100 test | Data | Training set of synthesized and testing set of captured synthesized color-and-depth image pairs, objects have size, shape, texture, and reflective characteristics, real-world data captured by Intel RealSense depth camera D415 and data generated from simulation for the 6D object pose | Pose Estimation |
| 2020 | Extended Monocular Image-based 3D Object Retrieval | Wenhui Li, Dan Song, Anan Liu, Weizhi Nie, Ting Zhang, Xiaoqian Zhao, Mingsheng Ma, Yuqian Li, Heyu Zhou dan.song@tju.edu.cn, anan0422@gmail.com |
Li et al. (2020) [pdf] |
40.000 2D images, 12.732 3D models, 40 categories<, train/test 50/50%/td> | - | Every category has between 52-500 3D models and 1000 2D images | Image-based Retrieval |
| 2021 | Retrieval of cultural heritage objects | Ivan Sipiran, Patrick Lazo, Cristian Lopezc, Milagritos Jimenez isipiran@dcc.uchile.cl |
Sipiran et al. (2021) | 938 3D models in 8 categories, train/test 70/30% | Data (1.8 GB) | Remeshed to 40.000 triangle faces, ground truth (shape, and culture) from curators, archaeological objects from Josefina Ramos de Cox Museum in Peru with varied geometry and artistic styles, scanned with structured-light desktop scanner | Textured Retrieval |
| 2021 | Retrieval and classification of protein surfaces equipped with physical and chemical properties | Andrea Raffoa, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia andrea.raffo@ge.imati.cnr.it, ulderico.fugacci@ge.imati.cnr.it, silvia.biasotti@ge.imati.cnr.it, walter.rocchia@iit.it |
Raffoa et al. (2021) | 5000 protein surfaces, 209 PDB entries, train/test 70/30% | - | Each PDB entry in different shapes experimentally determined via NMR measurements, based on the 2019 SHREC track | Protein Retrieval |
| 2021 | Surface-based protein domains retrieval | Florent Langenfeld, Matthieu Montes florent.langenfeld@lecnam.net |
Langenfeld et al. (2021) [pdf] |
554 molecular surfaces, 2 sets of 10 queries (with and without electro) | Website (3.2 GB) | 2 x 10 Pfam domains, surface mesh computed with EDTSurf, task is to compute 2x10 dissimilarity matrices | Protein Retrieval |
| 2021 | Classification in Cryo-Electron Tomograms | Ilja Gubins, Marten Chaillet, Gijs van der Schot, Remco C. Veltkamp, Friedrich G. Förster i.gubins@uu.nl |
Gubins et al. (2021) [pdf] |
10 tomograms, 512x512x512 1nm/voxel in 12 protein classes | Data (7.4 GB) | Generated by physics-based simulation, 1000-1300 proteins, 7-14 gold fiducials, 2-7 vesicles at random lacations and orientations without overlap per tomogram | Protein Classification |
| 2021 | Skeleton-based Hand Gesture Recognition in the Wild | Fabio Marco Caputo, Andrea Giachetti fabiomarco.caputo@univr.it |
Caputo et al. (2021) | 180 gesture sequences, 18 gesture classes, 40 occurences/class | Training (99MB), Test (59 MB) | Captured using a Leap Motion | Gesture Classification |
| 2021 | 3D Point Cloud Change Detection for Street Scenes | Tao Ku, Sam Galanakis, Bas Boom, Remco C. Veltkamp T.Ku@uu.nl |
Ku et al. (2021) | 78 large-scale street scene 3D point clouds, 866 object pairs, train/test 82/18% | Drive (1.6 GB) | Dataset provided by CycloMedia Technology, colored point clouds gathered in 2016 and 2020 in Schiedam with LiDAR sensors on vehicles, manual labeling | Point cloud Detection |
| 2021 | Quantifying Shape Complexity | Mazlum Ferhat Arslan, Alexandros Haridis, Paul L. Rosin, Sibel Tari ferhata@metu.edu.tr |
Ferhat Arslan et al. (2021) | set 1: 1800 models of cubes and spheres, set 2: 50 shapesof cuboids, set 3: 380 shapes | Drive (5.2 GB) | Set 1 and 2 generated, set 3 from Princeton Mesh Segmentation Benchmark | Complexity Classification |
| 2022 | Online detection of heterogeneous gestures | Marco Emporio, Anton Pirtac, Ariel Caputo, Marco Cristani, Andrea Giachetti andrea.giachetti@univr.it |
Emporio et al. (2022) | 16 classes gestures, 288 sequences, 3-5 gestures per sequence | Training (29 MB)Test (29 MB) | Captured using the Hololens 2 finger tracking headset in a realistic use-case of mixed reality interaction | Gesture Classification |
| 2022 | Fitting and recognition of simple geometric primitives on point clouds | Chiara Romanengo, Andrea Raffo chiara.romanengo@ge.imati.cnr.it |
Romanengo et al. (2022) | 46.925 point clouds in 5 classes, 46000 train, 925 test | Webpage (7.6 GB) | Point clouds generated from surfaces, then perturbed in 10 ways | Point cloud Fitting |
| 2022 | Open-Set 3D Object Retrieval using Multi-Modal Representation | Yue Gao, Yifan Feng, Xibin Zhao, Yandong Guo evanfeng97@gmail.com |
Feng et al. (2022) | 12309 objects in 40 classes, 2822 from 8 categories training set, 960 query objects and 8527 target objects from 32 other categories | OS-MN40 (46 GB), OS-MN40-Miss (28 GB) | Multimodal and multi-resolution, based on ModelNet40, manually split the categories in the train and retrieval set to simulate an open-set, also split into with/without modality. | Multimodal Retrieval |
| 2022 | Pothole and crack detection on road pavement using RGB-D images | A. Ranieri, E. Moscoso Thompson, S. Biasotti elia.moscoso@ge.imati.cnr.it |
Moscoso Thompson et al. (2022) | 4340 image pairs 797 RGB-D videos, 3340 training, 496 validation, 504 test images | Mendeley Data (6.39 GB) | Image set based on data sets Crack500, GAPs384, EdmCrack600, Pothole-600, Cracks and Potholes in Road Images Dataset, RGB-D video clips made with Luxonis OAK-D camera | Relief Detection |
| 2022 | Sketch-Based 3D Shape Retrieval in the Wild | Jie Qin, Shuaihang Yuan, Jiaxin Chen, Boulbaba Ben Amor, Yi Fang qinjiebuaa@gmail.com |
Qin et al. (2022) | 46000 CAD-based point cloud models, 1700 scanned point cloud models, 145000 sketches | Drive | Based on QuickDraw sketches, ModelNet40 and ShapeNet Core55 meshes, ScanObjectNN scans | Sketch Retrieval |
| 2022 | Protein-ligand binding site recognition | Luca Gagliardi, Walter Rocchia, Andrea Raffo, Ulderico Fugacci, Silvia Biasotti andrea.raffo@ge.imati.cnr.it |
Gagliardi et al. (2022) | 1091 proteins, 1721 ligand binding sites | Github (1.8 GB) | PQR file using AMBER force field is created using pdb2pqr software, meshes created with NanoShaper | Protein Recognition |
| 2023 | Detection of symmetries on 3D point clouds representing simple shapes | Ivan Sipiran, Chiara Romanengo, Silvia Biasotti, Bianca Falcidieno isipiran@dcc.uchile.cl |
Sipiran et al. (2023) [pdf] |
69.000 Pointclouds of simple shapes (60k train, 9k test), 5 perturbations, 7-curve forms | Drive (7.9 GB) | Curve forms generate cylinders and cones, transformed to point clouds, perturbed with noise | Symmetry Detection |
| 2023 | Sketch-based 3D Animal Fine-Grained Retrieval | Trung-Nghia Le, Minh-Triet Tran, Minh-Quan Le, Xuan-Nhat Hoang, Thang-Long Nguyen-Ho, Trong-Thuan Nguyen, Trong-Le Do, Vinh-Tiep Nguyen, Tam V. Nguyen, Akihiro Sugimoto ltnghia@fit.hcmus.edu.vn, tmtriet@fit.hcmus.edu.vn |
Le et al. (2023) | 711 animal models, 140 sketch queries | Drive (229 MB) | Collected models are made watertight and reduced, sketches are made manually | Sketch Retrieval |
| 2023 | Text-based 3D Animal Fine-Grained Retrieval | Trung-Nghia Le, Minh-Triet Tran, Minh-Quan Le, Xuan-Nhat Hoang, Thang-Long Nguyen-Ho, Trong-Thuan Nguyen, Trong-Le Do, Vinh-Tiep Nguyen, Tam V. Nguyen, Akihiro Sugimoto ltnghia@fit.hcmus.edu.vn, tmtriet@fit.hcmus.edu.vn |
Le et al. (2023) | 711 animal models, 150 sentences as queries | Drive (227 MB) | Collected models are made watertight and reduced, text descriptions are made manually | Text Retrieval |
| 2023 | Point Cloud Change Detection for City Scenes | Honglin Yuan, Yang Gao, Tao Ku, Remco C. Veltkamp hlyuan@nuist.edu.cn |
Gao et al. (2023) | 1711 object change pairs, 78 city-scenes, 5 change classes, train/test 80/20% | Data (8,94 GB) | LiDAR data is from SHREC 2021 track, simulated data created with Unreal Engine 4, labels manually | Range scan Detection |
| 2024 | Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions | Florent Langenfeld, Matthieu Montes florent.langenfeld@cnam.fr, matthieu.montes@cnam.fr |
Yacoub et al. (2024) [pdf] |
387 query and 520 target surfaces, 52 protein-protein interactions | Queries (1 GB), Targets (1.2 G) |
Based on docking benchmark version 5 | Protein Retrieval |
| 2024 | Recognition of hand motions molding clay | Ben Veldhuijzen, Remco C. Veltkamp b.veldhuijzen@students.uu.nl |
Veldhuijzen et al. (2024) | 62 motion sequences (between 29 and 3721 frames) in 7 classes, training/test 70/30% | Data | Capturing an experienced potter with a Vicon System containing 14 Vantage cameras | Gesture Classification |
| 2025 | Partial Retrieval Benchmark | Bart Iver van Blokland, Ivan Sipiran, Benjamin Bustos, Silvia Biasotti, Giorgio Palmieri bart.van.blokland@ntnu.no |
van Blokland et al. (2025) | dynamic | Github | ShapeBench generates point clouds dynamically by sampling mesh data from Objaverse-1.0, which contains over 800,000 3D objects | Partial Retrieval |
| 2025 | Protein Shape Classification | Taher Yacoub, Camille Depenveiller, Matthieu Montès taher.yacoub@lecnam.net |
Yacoub et al. (2025) | 11,565 surfaces divided into 97 imbalanced classes, train/test 80/20% | Training (4.1 GB), Test (1 GB) | Surfaces generated with NanoShaper, the potentials are calculated using APBS | Protein Classification |
| 2025 | Retrieval and Segmentation of Multiple Relief Patterns | Gabriele Paolini, Claudio Tortorici, Stefano Berretti gabriele.paolini1@unifi.it |
Paolini et al. (2025) [pdf] |
700 training meshes, 300 test meshes, 54 query meshes | GitHub | 15 base models | Relief Retrieval |
| 2025 | 3D Object Retrieval & Completion with Gaussian Splatting Organizers | Thien-Phuc Tran, Minh-Quang Nguyen, Minh-Triet Tran, Tam V. Nguyen, Minh Do, Trong-Thuan Nguyen, Viet-Tham Huynh ttphuc21@apcs.fitus.edu.vn |
Tran et al. (2025) | 542 synthetic 3D objects in 107 semantic classes, 7705 pre-segmented Guassian Splatting clusters | Upon request | Meshes converted to Gaussian Splatting representations | Part-based Retrieval |
| 2025 | Intelligent 3D Room Design | Trong-Thuan Nguyen, Viet-Tham Huynh, Minh-Triet Tran, Tam V. Nguyen ntthuan@selab.hcmus.edu.vn |
Nguyen et al. (2025) | Over 1,600 apartment scenes, nearly 5,200 rooms, and more than 44,000 targeted queries | GitHub | 3D models are converted to multiple views and point clouds | Layout Classification |
| 20xx | Track | Organizers Contact |
Ref | DB size | Download (size) | Data Type & Acquisition | Category Task |