Fast 3D point clouds retrieval for Large-scale 3D Place Recognition
This work addresses the challenge of fast retrieval in 3D point clouds for applications like place recognition, but it is incremental as it adapts an existing method to a new domain.
The paper tackled the problem of accelerating 3D point cloud retrieval for large-scale place recognition by adapting the Differentiable Search Index (DSI) from text to 3D data, resulting in direct retrieval in constant time and evaluation on a public benchmark against state-of-the-art methods for quality and speed.
Retrieval in 3D point clouds is a challenging task that consists in retrieving the most similar point clouds to a given query within a reference of 3D points. Current methods focus on comparing descriptors of point clouds in order to identify similar ones. Due to the complexity of this latter step, here we focus on the acceleration of the retrieval by adapting the Differentiable Search Index (DSI), a transformer-based approach initially designed for text information retrieval, for 3D point clouds retrieval. Our approach generates 1D identifiers based on the point descriptors, enabling direct retrieval in constant time. To adapt DSI to 3D data, we integrate Vision Transformers to map descriptors to these identifiers while incorporating positional and semantic encoding. The approach is evaluated for place recognition on a public benchmark comparing its retrieval capabilities against state-of-the-art methods, in terms of quality and speed of returned point clouds.