用知识蒸馏让3D特征描述子更快，实现近实时6D姿态估计。
Distilling 3D distinctive local descriptors for 6D pose estimation
- 用GeDi作为教师模型，训练更高效的学生产生局部特征。
- 在5个BOP数据集上推理速度大幅提升，性能仍具竞争力。
- 新损失函数解决非显著特征的弱监督问题，适合工业落地。
三维局部描述子对编码几何表面特性至关重要，是点云理解任务的核心。其中，GeDi展现出强大的零样本6D姿态估计能力，但因推理成本过高，难以应用于实际场景。本文提出一种知识蒸馏框架，通过训练高效的学生模型从GeDi教师模型中回归局部描述子，以保留其有效性同时显著提升效率。关键贡献包括：一种高效的大型训练流程，能在计算与存储受限下保持对遮挡和部分观测的鲁棒性；以及一种新型损失函数，可处理来自非显著教师描述子的弱监督信号。我们在五个BOP基准数据集上验证了该方法，实现了显著的推理时间降低，同时保持与现有方法相当的性能，使零样本6D姿态估计更接近实时应用。项目网站：https://tev-fbk.github.io/dGeDi/
原文摘要 · Abstract (English)
Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptors, GeDi has demonstrated strong zero-shot 6D pose estimation capabilities but remains computationally impractical for real-world applications due to its expensive inference process. Can we retain GeDi's effectiveness while significantly improving its efficiency? In this paper, we explore this question by introducing a knowledge distillation framework that trains an efficient student model to regress local descriptors from a GeDi teacher. Our key contributions include: an efficient large-scale training procedure that ensures robustness to occlusions and partial observations while operating under compute and storage constraints, and a novel loss formulation that handles weak supervision from non-distinctive teacher descriptors. We validate our approach on five BOP Benchmark datasets and demonstrate a significant reduction in inference time while maintaining competitive performance with existing methods, bringing zero-shot 6D pose estimation closer to real-time feasibility. Project Website: https://tev-fbk.github.io/dGeDi/
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