Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
ACM Transactions on Graphics (TOG)
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
Retrieving 3D shapes based on their appearance
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Augmented Reeb Graphs for Content-Based Retrieval of 3D Mesh Models
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Digital photography with flash and no-flash image pairs
ACM SIGGRAPH 2004 Papers
Efficient 3D object retrieval using depth images
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A new 3D model retrieval approach based on the elevation descriptor
Pattern Recognition
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Depth image-based representation and compression for static and animated 3-D objects
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose an efficient method for 3D model retrieval using a single depth image. Unlike existing algorithms that use a complete 3D model or a user sketch as input queries, a single depth image is used as an input query, which can be captured easily with an off-the-shelf lowcost 3D camera, such as a Kinect camera. 3D models in the database are represented by multiple depth images acquired from adaptively sampled viewpoints. The proposed algorithm can retrieve relevant 3D models while considering local 3D geometric characteristics using a rotation-invariant feature descriptor. The proposed method consists of three steps: preprocessing, multiple depth image based representation (M-DIBR), and description of 3D models, and similarity measurement and comparison. Experimental results demonstrate that the proposed algorithm is convenient to use and its performance is comparable to recent algorithms in terms of retrieval accuracy and speed.