Monocular Estimation of Translation, Pose and 3D Shape on Detected Objects using a Convolutional Autoencoder

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Ivar Persson, Martin Ahrnbom, Mikael Nilsson

Published: 16 Feb 2022, Last Modified: 12 Nov 2025Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISAPPEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper present a 6DoF-positioning method and shape estimation method for cars from monocular images. We pre-learn principal components, using Principal Component Analysis (PCA), from the shape of cars and use a learnt encoder-decoder structure in order to position the cars and create binary masks of each camera instance. The proposed method is tailored towards usefulness for autonomous driving and traffic safety surveillance. The work introduces a novel encoder-decoder framework for this purpose, thus expanding and extending state-of-the-art models for the task. Quantitative and qualitative analysis is performed on the Apolloscape dataset, showing promising results, in particular regarding rotations and segmentation masks.