Point cloud upsampling via a coarse-to-fine network with transformer-encoder

Point clouds provide a common geometric representation for burgeoning 3D graphics and vision tasks. To deal with the sparse, noisy and non-uniform output of most 3D data acquisition devices, this paper presents a novel coarse-to-fine learning framework that incorporates the Transformer-encoder and positional feature fusion. Its long-range dependencies with sensitive positional information allow robust feature embedding and fusion of points, especially noising elements and non-regular outliers. The proposed network consists of a Coarse Points Generator and a Points Offsets Refiner. The generator embodies a multi-feature Transformer-encoder and an EdgeConv-based feature reshaping to infer the coarse but dense upsampling point sets, whereas the refiner further learns the positions of upsampled points based on multi-feature fusion strategy that can adaptively adjust the fused features’ weights of coarse points and points offsets. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts. Our code is publicly available at https://github.com/Superlyxi/CFT-PU.