The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.
Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images.
Data set: The query set is composed of 120 range images, which are acquired by capturing 3 range scans of 40 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (*.off) representing the scan in a triangular mesh.
The target database contains 800 complete 3D models, which are categorized into 40 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (*.off).
Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).
Please Cite the Paper:
Dutagaci H, Godil A, Cheung CP, Furuya T, Hillenbrand U, Ohbuchi R. SHREC'10 Track: Range Scan Retrieval. In3DOR 2010 May 2 (pp. 109-115). http://dx.doi.org/10.2312/3DOR/3DOR10/109-115
About this Dataset
| Title | SHREC'10 track: Range scan retrieval |
|---|---|
| Description | The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database. Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images. Data set: The query set is composed of 120 range images, which are acquired by capturing 3 range scans of 40 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (*.off) representing the scan in a triangular mesh. The target database contains 800 complete 3D models, which are categorized into 40 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (*.off). Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2). Please Cite the Paper: Dutagaci H, Godil A, Cheung CP, Furuya T, Hillenbrand U, Ohbuchi R. SHREC'10 Track: Range Scan Retrieval. In3DOR 2010 May 2 (pp. 109-115). http://dx.doi.org/10.2312/3DOR/3DOR10/109-115 |
| Modified | 2010-02-02 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | 3D Shape Retrieval , 3D Models , 3D Range Scans , Evaluation |
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"title": "SHREC'10 track: Range scan retrieval",
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