A 3D Shape Benchmark for Retrieval and Automatic Classification of Architectural Data

This time, three track were run: generic shape retrieval, querying with partial 3D models, and structural retrieval of watertight models.. Categories and Subject Descriptors

The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on a large Generic benchmark based on the Google 3D Warehouse1. We hope that

To this end, we compare three approaches to se- lect a specific set of eigenvalues such that the corresponding shape classification error on the input benchmark data set is

The aim of SHREC is to evaluate the performance of existing 3D shape retrieval algorithms, by highlighting their strengths and weaknesses, using a common test collection that allows

Here, we present expansion and verification to improve clas- sification when few training examples are available. 2 results are shown for when: 1) standard classification (ST)

The objective of the SHREC’15 Range Scans based 3D Shape Retrieval track is to evaluate algorithms that match range scans of real objects to complete 3D mesh models in a

In the 2015 SHREC track on Scalability of 3D Shape Retrieval we provide a benchmark with more than 96 thousand shapes.. The data set is based on a non-rigid retrieval benchmark

Our methodology allows generating large-scale fragment test data sets from existing CH object models, complementing manual benchmark generation based on scanning of fragmented