Distinctiveness oriented Positional Equilibrium for Point Cloud Registration

Recent state-of-the-art learning-based approaches to point cloud registration have largely been based on graph neural networks (GNN). However, these prominent GNN backbones suffer from the indistinguishable features problem associated with oversmoothing and structural ambiguity of the high-level features, a crucial bottleneck to point cloud registration that has evaded scrutiny in the recent relevant literature. To address this issue, we propose the Distinctiveness oriented Positional Equilibrium (DoPE) module, a novel positional embedding scheme that significantly improves the distinctiveness of the high-level features within both the source and target point clouds, resulting in superior point matching and hence registration accuracy. Specifically, we use the DoPE module in an iterative registration framework, whereby the two point clouds are gradually registered via rigid transformations that are computed from DoPE's position-aware features. With every successive iteration, the DoPE module feeds increasingly consistent positional information to would-be corresponding pairs, which in turn enhances the resulting point-to-point correspondence predictions used to estimate the rigid transformation. Within only a few iterations, the network converges to a desired equilibrium, where the positional embeddings given to matching pairs become essentially identical. We validate the effectiveness of DoPE through comprehensive experiments on various registration benchmarks, registration task settings, and prominent backbones, yielding unprecedented performance improvement across all combinations.