Visual Place Recognition Based on an Adaptive D-Value Optimization Strategy

EigenPlaces is a state-of-the-art visual place recognition (VPR) method that constructs training classes via SVD-based focal points, where a fixed focal distance D controls how far the focal point is placed from each cell center. However, this globally fixed D cannot adapt to the diverse scene geometries encountered across different urban environments. In this work, we systematically analyze the sensitivity of D across multiple benchmark datasets and reveal that the optimal D value is highly dataset-dependent, with performance gaps of up to 4.4 percentage points between the best and worst D choices. We then propose a depth-aware adaptive D strategy that leverages monocular depth estimation to compute per-cell focal distances, combined with quantile mapping to ensure sufficient variance in the assigned D values. By establishing a principled connection between visual sensor data and geometric training supervision, our method enhances the environmental perception reliability of intelligent sensing platforms. Experiments on three benchmarks (Pitts30k, AmsterTime, SF-XL) validate the dataset-dependent nature of D and confirm that our depth-aware approach achieves the best same-distribution performance among all tested configurations. We further conduct a multi-strategy ablation comparing depth raw, depth quantile, and SVD eigenvalue ratio approaches, providing practical guidance for adaptive focal distance selection in VPR training pipelines.