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Place Recognition is a crucial capability for mobile robot localization andnavigation. Image-based or Visual Place Recognition (VPR) is a challengingproblem as scene appearance and camera viewpoint can change significantly whenplaces are revisited. Recent VPR methods based on ``sequentialrepresentations'' have shown promising results as compared to traditionalsequence score aggregation or single image based techniques. In parallel tothese endeavors, 3D point clouds based place recognition is also being exploredfollowing the advances in deep learning based point cloud processing. However,a key question remains: is an explicit 3D structure based place representationalways superior to an implicit ``spatial'' representation based on sequence ofRGB images which can inherently learn scene structure. In this extendedabstract, we attempt to compare these two types of methods by considering asimilar ``metric span'' to represent places. We compare a 3D point cloud basedmethod (PointNetVLAD) with image sequence based methods (SeqNet and others) andshowcase that image sequence based techniques approach, and can even surpass,the performance achieved by point cloud based methods for a given metric span.These performance variations can be attributed to differences in data richnessof input sensors as well as data accumulation strategies for a mobile robot.While a perfect apple-to-apple comparison may not be feasible for these twodifferent modalities, the presented comparison takes a step in the direction ofanswering deeper questions regarding spatial representations, relevant toseveral applications like Autonomous Driving and Augmented/Virtual Reality.Source code available publicly https://github.com/oravus/seqNet.

SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition