Calculating Precision-Recall (PR) Curve for 3D Model Retrieval Results

To conduct a comprehensive performance evaluation of 3D model retrieval systems, calculating the Precision-Recall (PR) curve serves as a crucial analytical tool. The PR curve graphically illustrates the trade-off between precision and recall metrics at various retrieval thresholds. This curve is generated by plotting precision values against their corresponding recall values while systematically adjusting the similarity threshold parameters. The implementation typically involves sorting retrieval results by similarity scores, then calculating precision and recall at each threshold level using cumulative true positive counts. Key algorithmic steps include: computing true positives (TP), false positives (FP), and false negatives (FN) for each threshold; deriving precision as TP/(TP+FP) and recall as TP/(TP+FN); and generating smooth curves through interpolation techniques. Through PR curve analysis, developers can optimize retrieval parameters by identifying optimal operating points where both precision and recall achieve satisfactory balance, ultimately enhancing retrieval system performance.