The article details the development of Panopticus, a multi-branch approach for omnidirectional 3D object detection. Initial experiments based on the BEVDet model revealed insights into the importance of model design, including various ResNet backbones and DepthNet configurations. The study employed a comprehensive setup using the nuScenes dataset, assessing detection capabilities via average precision and true positive error metrics. The findings underscored the model's robustness and adaptive execution scheduling, presenting significant potential in enhancing detection accuracy within diverse environments.
In-depth observations on enhancing the BEVDet model revealed significant insights and challenges for developing Panopticus, a model designed for superior 3D object detection.
Sixteen BEVDet model variants, derived from a baseline, utilized different ResNet backbones, input resolutions, and DepthNets to improve detection capabilities and accuracy.
We compared our model's efficacy using average precision (AP) as a pivotal evaluation metric, showcasing improvements over state-of-the-art techniques in 3D object detection.
The experiment setup profiled 850 nuScenes dataset scenes, utilizing 360° FOV from six camera images, highlighting the importance of data diversity in model training.
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