  Point clouds offer comprehensive and precise data regarding the contour and
configuration of objects. Employing such geometric and topological 3D
information of objects in class incremental learning can aid endless
application in 3D-computer vision. Well known 3D-point cloud class incremental
learning methods for addressing catastrophic forgetting generally entail the
usage of previously encountered data, which can present difficulties in
situations where there are restrictions on memory or when there are concerns
about the legality of the data. Towards this we pioneer to leverage exemplar
free class incremental learning on Point Clouds. In this paper we propose
PointCLIMB: An exemplar Free Class Incremental Learning Benchmark. We focus on
a pragmatic perspective to consider novel classes for class incremental
learning on 3D point clouds. We setup a benchmark for 3D Exemplar free class
incremental learning. We investigate performance of various backbones on
3D-Exemplar Free Class Incremental Learning framework. We demonstrate our
results on ModelNet40 dataset.
