  3D object classification has been widely-applied into both academic and
industrial scenarios. However, most state-of-the-art algorithms are facing with
a fixed 3D object classification task set, which cannot well tackle the new
coming data with incremental tasks as human ourselves. Meanwhile, the
performance of most state-of-the-art lifelong learning models can be
deteriorated easily on previously learned classification tasks, due to the
existing of unordered, large-scale, and irregular 3D geometry data. To address
this challenge, in this paper, we propose a Lifelong 3D Object Classification
(i.e., L3DOC) framewor, which can consecutively learn new 3D object
classification tasks via imitating 'human learning'. Specifically, the core
idea of our proposed L3DOC model is to factorize PointNet in a perspective of
lifelong learning, while capturing and storing the shared point-knowledge in a
perspective of layer-wise tensor factorization architecture. To further
transfer the task-specific knowledge from previous tasks to the new coming
classification task, a memory attention mechanism is proposed to connect the
current task with relevant previously tasks, which can effectively prevent
catastrophic forgetting via soft-transferring previous knowledge. To our best
knowledge, this is the first work about using lifelong learning to handle 3D
object classification task without model fine-tuning or retraining.
Furthermore, our L3DOC model can also be extended to other backbone network
(e.g., PointNet++). To the end, comparisons on several point cloud datasets
validate that our L3DOC model can reduce averaged 1.68~3.36 times parameters
for the overall model, without sacrificing classification accuracy of each
task.
