Abstract:
Deep learning in computer vision domain plays an important role in the last
few years. Its performance surpasses previous approaches based on handcrafted features. Deep learning based approaches yield better performance
in scene understanding tasks. Scene understanding tasks have great importance in the robot navigation for the indoor and outdoor environment.
Recently, scene understanding tasks captured the attention of the researchers
towards 3D domain, because 3D data provides rich information of geometry
about the objects and scenes. Deep learning due to its power of extracting
discriminative hierarchical features from input became popular in the 3D do main. 3D point clouds are compact and computationally less expensive as
compare to volumetric 3D data. 3D point clouds are memory efficient too.
Recently, fully pointwise convolutional neural network has been designed for
unstructured 3D point clouds [1]. The designed 3D kernel can be placed on
each point in 3D point cloud. But training this network as deeper network
was an open challenge because it caused performance degradation. But this
degradation problem can be resolved with training it through residual learning, because residual learning allows the network to become deeper without
sacrificing performance. Here in this paper we train deep pointwise CNN using residual learning and presented different experiments. Training of deep
architecture with residual learning in the domain of unstructured 3D point
clouds is still not performed before. Our qualitative results show that we are
successfully able to overcome this problem of performance degradation due
to network depth. We evaluated our deep network on scene segmentation
and object recognition task. On scene segmentation task, for Stanford 3D
indoor scene(S3DIS) dataset our deep network gives competitive performance
as compare to many existing state-of-the-art architectures.
