CLIP-based Point Cloud Classification via Point Cloud to Image Translation

TL;DR

This paper introduces PPCITNet, a novel method that translates point clouds into colored images with visual cues, enhancing CLIP-based classification accuracy for 3D point cloud data.

Contribution

It proposes a point cloud to image translation network and a viewpoint adapter, addressing limitations of existing CLIP-based methods for improved classification.

Findings

Outperforms state-of-the-art CLIP-based models on multiple datasets

Achieves higher accuracy in point cloud classification tasks

Demonstrates the effectiveness of visual cues in point cloud understanding

Abstract

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.