Benchmarks and state-of-the-art methods for semantic segmentation of 3D meshes? I'm wondering what benchmarks there exist for semantic segmentation of 3D meshes? I have already found "A Benchmark for 3D Mesh Segmentation"; is this currently the only benchmark that exists for 3D mesh segmentation?
Also, is there some list of state-of-the-art methods for semantic segmentation of 3D meshes, like there is for some other benchmarks?
 A: Update:
Currently there seems to be a preference for using point clouds over raw meshes for 3d segmentation. They can use all the benchmarks below plus:


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*S3DIS
URL: http://buildingparser.stanford.edu/dataset.html

*ScanNet
URL: http://www.scan-net.org/
The state of the art seems to be [11] closely followed by [10] and both use PointNet++ [12].

The one you are refering to is the common PSB (Princeton Segmentation Benchmark) dataset [1].
Other Important datasets used for 3D mesh segmentation are: 


*

*COSEG [2]


*

*URL: http://irc.cs.sdu.edu.cn/~yunhai/public_html/ssl/ssd.htm


*ShapeNet [3]


*

*URL: https://www.shapenet.org/


*Other from Princeton [4]


*

*URL: http://shape.cs.princeton.edu/vkcorrs/papers_small/13_SIGGRAPH_CorrsTmplt.pdf


*Other basen on subset of ShapeNet [5]


*

*URL: https://cs.stanford.edu/~ericyi/project_page/part_annotation/
On the other hand, there seems to be no state-of-the-art page for 3D shape segmentation. But the papers I find most important are [6, 7, 8, 9], with 9 the state of the art.

Refs:


*

*Chen et al: A Benchmark for 3D Mesh Segmentation, 2009

*Wang et al: 
Active co-analysis of a set of shapes, 2012

*Kim et al: Learning Part-based Templates from Large Collections of 3D Shapes, 2013

*Chang et al: ShapeNet: An Information-Rich 3D Model Repository, 2015

*Yi et al: A scalable active framework for region annotation in 3d shape collections, 2016

*Kalogerakis et al: Learning 3D Mesh Segmentation and Labeling, 2010

*Guo et al: 3D Mesh Labeling via Deep Convolutional Neural Networks, 2015

*Kalogerakis et al: 3D Shape Segmentation with Projective Convolutional Networks, 2017 

*Wang et al: 3D shape segmentation via shape fully convolutional networks, 2018

*Wang et al: Associatively Segmenting Instances and Semantics in Point Clouds, 2019

*Yang et al: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds, 2019

*Qi et al: PointNet++: Deep Hierarchical Feature Learning on
Point Sets in a Metric Space, 2017

