View-aligned pixel-level feature aggregation for 3D shape classification

Abstract

Multi-view 3D shape classification, which identifies a 3D shape based on its 2D views rendered from different viewpoints, has emerged as a promising method of shape understanding. A key building block in these methods is cross-view feature aggregation. However, existing methods dominantly follow the “extract-then-aggregate” pipeline for view-level global feature aggregation, leaving cross-view pixel-level feature interaction under-explored. To tackle this issue, we develop a “fuse-while-extract” pipeline, with a novel View-aligned Pixel-level Fusion (VPF) module to fuse cross-view pixel-level features originating from the same 3D part. We first reconstruct the 3D coordinate of each feature via the rasterization results, then match and fuse the features via spatial neighbor searching. Incorporating the proposed VPF module with ResNet18 backbone, we build a novel view-aligned multi-view network, which conducts feature extraction and cross-view fusion alternatively. Extensive experiments have demonstrated the effectiveness of the VPF module as well as the excellent performance of the proposed network.