3D Object Reconstruction with Deep Learning

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dc.contributor.authorAremu, Stephen S.
dc.contributor.authorTaherkhani, Aboozar
dc.contributor.authorLiu, Chang
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2024-01-02
dc.date.accessioned2024-02-12T16:35:37Z
dc.date.available2024-02-12T16:35:37Z
dc.date.issued2024-05-06
dc.description.abstractRecent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh re-construction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score.
dc.funderNo external funder
dc.identifier.citationAremu, S.S., Taherkhani, A., Liu, C. and Yang, S. (2024) 3D Object Reconstruction with Deep Learning. IFIP Advances in Information and Communication Technology,
dc.identifier.doihttps://doi.org/10.1007/978-3-031-57919-6_12
dc.identifier.urihttps://hdl.handle.net/2086/23534
dc.language.isoen
dc.peerreviewedYes
dc.publisherSpringer
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.title3D Object Reconstruction with Deep Learning
dc.typeConference

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