PlaneRecTR++:用于联合3D平面重建和姿势估计的统一查询学习
IEEE transactions on pattern analysis and machine intelligence
|September 16, 2025
概括
本研究介绍了PlaneRecTR++,这是一个统一的变压器框架,用于3D平面重建和姿势估计. 通过整合多个子任务,它实现了最先进的性能,而不需要初始姿势或对应数据.
科学领域:
- 计算机视觉 计算机视觉
- 三维重建的3D重建
- 机器学习 机器学习
背景情况:
- 3D平面重建涉及复杂的子任务,如平面检测,细分和姿势估计.
- 以前的方法使用了一种连续的多阶段方法,通过单独处理子任务来限制性能.
- 现有的技术往往需要外部平面对应标签和初始姿势估计.
研究的目的:
- 开发一个统一的,单个阶段的框架,用于多视图3D平面重建和构成估计.
- 克服现有方法中分别处理子任务的局限性.
- 消除了初始摄像头姿势估计和平面对应监督的需要.
主要方法:
- 拟议的PlaneRecTR++,是一种基于变压器的新型架构.
- 集成的框架智能平面检测,细分,参数回归和跨框架对应到一个单一的模型中.
- 采用基于查询的学习方法来增强语义实体之间的推理.
主要成果:
- 通过统一的学习,在集成的子任务中实现了相互利益.
- 在基准数据集上建立了一个新的最先进的性能:ScanNetv1,ScanNetv2,NYUv2-Plane和MatterPort3D.
- 证明了大量的定量和质量的改进.
结论:
- 统一的单阶段框架有效地解决了多视图的3D平面重建和立场估计.
- 整合密切相关的子任务会比顺序方法带来更高的性能.
- PlaneRecTR++为3D场景理解提供了一个更有效,更准确的解决方案.
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