MonoMAE：通过深度感知的掩码自编码器增强单目3D检测
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
AI总结：
提出受掩码自编码器启发的单目3D检测器MonoMAE，通过深度感知掩码和轻量级查询补全机制解决目标遮挡问题，学习丰富且具泛化能力的3D表征，显著提升遮挡与非遮挡目标的检测性能。
摘要：
单目3D目标检测旨在从单视图图像中实现目标的精确3D定位与识别。尽管近期取得进展，但在处理普遍的目标遮挡问题时仍常显吃力，这往往会使目标尺寸、深度和方向的预测变得复杂并导致性能下降。我们设计了MonoMAE，一种受掩码自编码器启发的单目3D检测器，通过在特征空间中掩码和重建目标来解决目标遮挡问题。MonoMAE包含两项新颖设计。其一是深度感知掩码，它在特征空间中选择性地掩码非遮挡目标查询的特定部分，以模拟遮挡目标查询用于网络训练。它通过根据深度信息自适应地平衡掩码和保留的查询部分来掩码非遮挡目标查询。其二是轻量级查询补全，它与深度感知掩码协同工作，学习重建和补全被掩码的目标查询。通过所提出的目标遮挡与补全机制，MonoMAE学习到丰富的3D表征，在定性和定量上均对遮挡和非遮挡目标实现了卓越的单目3D检测性能。此外，MonoMAE学习到具有泛化能力的表征，能在新领域中良好工作。
英文摘要：
Monocular 3D object detection aims for precise 3D localization and identification of objects from a single-view image. Despite its recent progress, it often struggles while handling pervasive object occlusions that tend to complicate and degrade the prediction of object dimensions, depths, and orientations. We design MonoMAE, a monocular 3D detector inspired by Masked Autoencoders that addresses the object occlusion issue by masking and reconstructing objects in the feature space. MonoMAE consists of two novel designs. The first is depth-aware masking that selectively masks certain parts of non-occluded object queries in the feature space for simulating occluded object queries for network training. It masks non-occluded object queries by balancing the masked and preserved query portions adaptively according to the depth information. The second is lightweight query completion that works with the depth-aware masking to learn to reconstruct and complete the masked object queries. With the proposed object occlusion and completion, MonoMAE learns enriched 3D representations that achieve superior monocular 3D detection performance qualitatively and quantitatively for both occluded and non-occluded objects. Additionally, MonoMAE learns generalizable representations that can work well in new domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
