# Convolutional Neural Networks for joint object detection and pose estimation: A comparative study Source: [Convolutional Neural Networks for joint object detection and pose estimation: A comparative study]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:30.149Z Authors: Francisco Massa, Mathieu Aubry, Renaud Marlet Published: 2014-12-22T22:26:26.000Z ## Abstract In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose. We identify different feature representations of oriented objects, and energies that lead a network to learn this representations. The choice of the representation is crucial since the pose of an object has a natural, continuous structure while its category is a discrete variable. We evaluate the different approaches on the joint object detection and pose estimation task of the Pascal3D+ benchmark using Average Viewpoint Precision. We show that a classification approach on discretized viewpoints achieves state-of-the-art performance for joint object detection and pose estimation, and significantly outperforms existing baselines on this benchmark. ## 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]()