Performances on standard 3D point cloud benchmarks have plateaued, resulting in oversized models and complex network design to make a fractional improvement. We present an alternative to enhance existing deep neural networks without any redesigning or extra parameters, termed as Spatial-Neighbor Adapter (SN-Adapter). Building on any trained 3D network, we utilize its learned encoding capability to extract features of the training dataset and summarize them as prototypical spatial knowledge. For a test point cloud, the SN-Adapter retrieves k nearest neighbors (k-NN) from the pre-constructed spatial prototypes and linearly interpolates the k-NN prediction with that of the original 3D network. By providing complementary characteristics, the proposed SN-Adapter serves as a plug-and-play module to economically improve performance in a non-parametric manner. More importantly, our SN-Adapter can be effectively generalized to various 3D tasks, including shape classification, part segmentation, and 3D object detection, demonstrating its superiority and robustness. We hope our approach could show a new perspective for point cloud analysis and facilitate future research.

翻译：标准三维点云基准性能已趋于饱和，导致模型过度臃肿且网络设计复杂化，仅能实现微小改进。我们提出一种无需重新设计或额外参数的替代方案——空间邻居适配器（SN-Adapter）。基于任意已训练的3D网络，利用其学习到的编码能力提取训练数据集的特征，并将其归纳为原型空间知识。对于测试点云，SN-Adapter从预构建的空间原型中检索k个最近邻（k-NN），并通过线性插值融合k-NN预测结果与原始3D网络的输出。通过提供互补特征，所提出的SN-Adapter可作为即插即用模块，以无参数方式经济地提升性能。更重要的是，我们的SN-Adapter可有效泛化至多种3D任务，包括形状分类、部件分割和三维目标检测，展现了其优越性与鲁棒性。我们希望该方法能为点云分析提供新视角，并推动未来研究。