ComPoint: Can Complex-Valued Representation Benefit Point Cloud Place Recognition?

Abstract

“Where was this place?”-figuring out the location of a point cloud scene is a challenge that has attracted researchers in recent years, under the name of point cloud place recognition. Driven by the drastic acceleration of 3D data and corresponding technique forces, research in this field has witnessed remarkable progress. However, the existing methods are stuck in a dilemma, the limited capability of feature representations needs more complicated architectures to enhance the performance further. This inspires us to envision whether there is a better representation for this task. To explore its possibility, in this paper, we propose a new framework, dubbed ComPoint, in the form of complex-valued representations for large-scale point cloud place recognition. Theoretically, the framework is guided by two proven propositions where one implies that richer information provided by the complex-valued representations of point clouds can benefit the performance. Practically speaking, ComPoint is designed with three modules, with each module highlighting its different characteristics. First, Com-Transform guarantees informative data delivery by mining informative complex-valued representations of initial point clouds. Next, Com-Perception perceives and digs deeper into complex-valued features via a series of simple-design convolution blocks, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ComplexPointConv</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ComplexPointFT</i> . Then, Com-Fusion dynamic aggregates and interacts with the above features to obtain compact global complex-valued ones based on devised effective soft-balancing block in the VLAD network without involving extra memory footprint. Finally, our method is trained with proper strategies that are analyzed in-depth. The proposed method is witnessed to outperform the prior methods on four large-scale benchmarks quantitatively and qualitatively. It is also flexible plug-and-play in other approaches to improve their performance.