# RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving Source: [RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:57:21.263Z Authors: Jean Marie Uwabeza Vianney, Shubhra Aich, Bingbing Liu Published: 2019-11-21T19:21:32.000Z ## Abstract In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without much computational overhead, we propose a supervised and an unsupervised sparsification scheme of PseudoLiDAR prior to 3D detection. Both the strategies assist the standard 3D detector gain better performance over the raw PseudoLiDAR baseline using only ~5% of its points on the KITTI object detection benchmark, thus making our monocular framework and LiDAR-based counterparts computationally equivalent (Figure 1). Moreover, our architecture agnostic refinements provide state-of-the-art results on KITTI3D test set for "Car" and "Pedestrian" categories with 54% relative improvement for "Pedestrian". Finally, exploratory analysis is performed on the discrepancy between monocular and LiDAR-based 3D detection frameworks to guide future endeavours. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Bird-Eye-View (BEV) Object Detection on KITTI official (test)](): Bird-Eye-View (BEV) Object Detection; KITTI official (test); APBEV70 (Moderate): 17.6. - [3D Object Detection on KITTI official (test) (AP3D IoU=0.7)](): 3D Object Detection; KITTI official (test); AP3D@IoU=0.7 (Moderate): 1114. ## Original sources and implementation [Original paper]() [PDF]()