  In this paper, we propose a self-supervised learningmethod for multi-object
pose estimation. 3D object under-standing from 2D image is a challenging task
that infers ad-ditional dimension from reduced-dimensional information.In
particular, the estimation of the 3D localization or orien-tation of an object
requires precise reasoning, unlike othersimple clustering tasks such as object
classification. There-fore, the scale of the training dataset becomes more
cru-cial. However, it is challenging to obtain large amount of3D dataset since
achieving 3D annotation is expensive andtime-consuming. If the scale of the
training dataset can beincreased by involving the image sequence obtained
fromsimple navigation, it is possible to overcome the scale lim-itation of the
dataset and to have efficient adaptation tothe new environment. However, when
the self annotation isconducted on single image by the network itself,
trainingperformance of the network is bounded to the self perfor-mance.
Therefore, we propose a strategy to exploit multipleobservations of the object
in the image sequence in orderto surpass the self-performance: first, the
landmarks for theglobal object map are estimated through network predic-tion
and data association, and the corrected annotation fora single frame is
obtained. Then, network fine-tuning is con-ducted including the dataset
obtained by self-annotation,thereby exceeding the performance boundary of the
networkitself. The proposed method was evaluated on the KITTIdriving scene
dataset, and we demonstrate the performanceimprovement in the pose estimation
of multi-object in 3D space.
