标题： 使用格点变形框架和扩散增强合成数据的农业环境下的类别级6D物体位姿估计 显示英文标题

标题： Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data

摘要： 准确的6D物体姿态估计对于机器人抓取和操作至关重要，特别是在农业领域，其中水果和蔬菜在形状、大小和纹理方面表现出很高的类内变异性。 现有方法的绝大部分依赖于特定实例的CAD模型，或者需要深度传感器来解决几何模糊性，这使得它们在现实世界的农业应用中变得不切实际。 在这项工作中，我们引入了PLANTPose，这是一种新颖的框架，用于类别级别的6D姿态估计，完全基于RGB输入。 PLANTPose预测了相对于基础网格的6D姿态和变形参数，使单一的类别级别CAD模型能够适应未见过的实例。 这使得在不同形状上进行精确的姿态估计成为可能，而无需依赖特定实例的数据。 为了增强真实感并提高泛化能力，我们还利用Stable Diffusion通过真实的纹理细化合成训练图像，模仿由于成熟度和环境因素引起的变异，并弥合合成数据与现实世界之间的领域差距。 我们在一个具有挑战性的基准测试中的评估表明，我们的框架在处理大类内变化的同时保持了准确的6D姿态预测，显著优于最先进的基于RGB的方法MegaPose。 显示英文摘要

摘要： Accurate 6D object pose estimation is essential for robotic grasping and manipulation, particularly in agriculture, where fruits and vegetables exhibit high intra-class variability in shape, size, and texture. The vast majority of existing methods rely on instance-specific CAD models or require depth sensors to resolve geometric ambiguities, making them impractical for real-world agricultural applications. In this work, we introduce PLANTPose, a novel framework for category-level 6D pose estimation that operates purely on RGB input. PLANTPose predicts both the 6D pose and deformation parameters relative to a base mesh, allowing a single category-level CAD model to adapt to unseen instances. This enables accurate pose estimation across varying shapes without relying on instance-specific data. To enhance realism and improve generalization, we also leverage Stable Diffusion to refine synthetic training images with realistic texturing, mimicking variations due to ripeness and environmental factors and bridging the domain gap between synthetic data and the real world. Our evaluations on a challenging benchmark that includes bananas of various shapes, sizes, and ripeness status demonstrate the effectiveness of our framework in handling large intraclass variations while maintaining accurate 6D pose predictions, significantly outperforming the state-of-the-art RGB-based approach MegaPose.