# High-level camera-LiDAR fusion for 3D object detection with machine learning Source: [High-level camera-LiDAR fusion for 3D object detection with machine learning]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:48.053Z Authors: Gustavo A. Salazar-Gomez, Miguel A. Saavedra-Ruiz, Victor A. Romero-Cano Published: 2021-05-24T01:57:34.000Z ## Abstract This paper tackles the 3D object detection problem, which is of vital importance for applications such as autonomous driving. Our framework uses a Machine Learning (ML) pipeline on a combination of monocular camera and LiDAR data to detect vehicles in the surrounding 3D space of a moving platform. It uses frustum region proposals generated by State-Of-The-Art (SOTA) 2D object detectors to segment LiDAR point clouds into point clusters which represent potentially individual objects. We evaluate the performance of classical ML algorithms as part of an holistic pipeline for estimating the parameters of 3D bounding boxes which surround the vehicles around the moving platform. Our results demonstrate an efficient and accurate inference on a validation set, achieving an overall accuracy of 87.1%. ## 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. ## Original sources and implementation [Original paper]() [PDF]()