A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

Abstract

a b s t r a c tLarge scale 3D shape retrieval has become an important research direction in content based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale com prehensive and sketch based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query by Model or Query by Sketch 3D shape retrieval methods and to solicit state of the art approaches, we perform a more comprehensive comparison of twenty six (eighteen originally participating algorithms and eight additional state of the art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites

Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

“…Dex-Net 1.0 contains 13,252 3D mesh models: 8,987 from the SHREC 2014 challenge dataset [28], 2,539 from ModelNet40 [45], 1,371 from 3DNet [44], 129 from the KIT object database * [19], 120 from BigBIRD * [38], 80 from the Yale-CMU-Berkeley dataset * [8], and 26 from the Amazon Picking Challenge * scans ( * indicates laser-scanner data). We preprocess each mesh by removing unreferenced vertices, computing a reference frame with Principal Component Analysis (PCA) on the mesh vertices, setting the mesh center of mass z to the center of the mesh bounding box, and rescaling the synthetic meshes to fit the smallest dimension of the bounding box within w = 0.1m.…”

Learning best views of 3D shapes from sketch contour

“…In addition, hand-drawn sketches are used to model this concept. Thanks to the rapid development of sketch-based shape retrieval technology, lots of large-scale 3D shape datasets providing relevant sketches are available from different sources [4,14,15]. As illustrated in Fig.…”

Iterative 3D shape classification by online metric learning

“…We evaluate our iterative classification method on the large scale comprehensive 3D shape set from the SHape Retrieval Contest 2014 (Li et al, , 2015. The SHREC2014 collection is a complex database, which collects the relevant models from eight 3D object retrieval benchmarks.…”