# BirdNet: a 3D Object Detection Framework from LiDAR information Source: [BirdNet: a 3D Object Detection Framework from LiDAR information]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:01:12.135Z Authors: Jorge Beltran, Carlos Guindel, Francisco Miguel Moreno, Daniel Cruzado, Fernando Garcia, Arturo de la Escalera Published: 2018-05-03T09:59:45.000Z ## Abstract Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing. Finally, 3D oriented detections are computed in a post-processing phase. Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods. Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach. ## 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]()