Blogs

Point Cloud Neural Network (PCNN) plays an essential role in various 3D applications, with some of them even being time-sensitive and safety-critical. However, the large scale of unordered points with lengthy features results in heavy computational workloads, making them far from real-time processing. To address this challenge, we propose MoC,... [Read More]

Point Cloud Transformer is undergoing a rising trend in both industry and academia. It aligns traditional point cloud feature extraction methods with the latest transformer architecture and achieves remarkable performance. However, accelerators for traditional point cloud neural networks (PCNNs) and those solely for transformers fail to capture the characteristics of... [Read More]

Point-based Point Cloud Neural Networks (PCNNs) have attracted much attention for their higher accuracy than voxel-based and multi-view-based PCNNs. Nevertheless, the increasing scale of point cloud data poses a challenge for real-time processing. Numerous previous works focus on accelerating PCNN inference but only optimize specific stages, limiting their generality to... [Read More]