We explore long-term temporal visual correspondence-based optimization for 3D
video object detection in this work. Visual correspondence refers to one-to-one
mappings for pixels across multiple images. Correspondence-based optimization
is the cornerstone for 3D scene reconstruction but is less studied in 3D video
object detection, because moving objects violate multi-view geometry
constraints and are treated as outliers during scene reconstruction. We address
this issue by treating objects as first-class citizens during
correspondence-based optimization. In this work, we propose BA-Det, an
end-to-end optimizable object detector with object-centric temporal
correspondence learning and featuremetric object bundle adjustment.
Empirically, we verify the effectiveness and efficiency of BA-Det for multiple
baseline 3D detectors under various setups. Our BA-Det achieves SOTA
performance on the large-scale Waymo Open Dataset (WOD) with only marginal
computation cost. Our code is available at
this https URL

该研究探讨了长期时间视觉对应优化在三维视频对象检测中的应用，提出了具有目标中心时间对应学习和特征度量对象绑定调整的 BA-Det，取得了各种设置下基线 3D 检测器的 SOTA 性能。