  Point cloud understanding is an inherently challenging problem because of the
sparse and unordered structure of the point cloud in the 3D space. Recently,
Contrastive Vision-Language Pre-training (CLIP) based point cloud
classification model i.e. PointCLIP has added a new direction in the point
cloud classification research domain. In this method, at first multi-view depth
maps are extracted from the point cloud and passed through the CLIP visual
encoder. To transfer the 3D knowledge to the network, a small network called an
adapter is fine-tuned on top of the CLIP visual encoder. PointCLIP has two
limitations. Firstly, the point cloud depth maps lack image information which
is essential for tasks like classification and recognition. Secondly, the
adapter only relies on the global representation of the multi-view features.
Motivated by this observation, we propose a Pretrained Point Cloud to Image
Translation Network (PPCITNet) that produces generalized colored images along
with additional salient visual cues to the point cloud depth maps so that it
can achieve promising performance on point cloud classification and
understanding. In addition, we propose a novel viewpoint adapter that combines
the view feature processed by each viewpoint as well as the global intertwined
knowledge that exists across the multi-view features. The experimental results
demonstrate the superior performance of the proposed model over existing
state-of-the-art CLIP-based models on ModelNet10, ModelNet40, and ScanobjectNN
datasets.
