小隔间 发表于 2025-3-26 21:20:02
LTS-NET: End-to-End Unsupervised Learning of Long-Term 3D Stable Objects specifically distinguishing between static and dynamic objects. Understanding object stability is key for mobile robots since long-term stable objects can be exploited as landmarks for long-term localisation. Our pipeline includes a labelling method that utilizes historical data from the environmenDefault 发表于 2025-3-27 04:01:12
EA-Repose: Efficient and Accurate Feature-Metric-Based 6D Object Pose Refinement via Deep Reinforcemthm as the solver. However, they may easily ignore the importance of the damping parameter . factor, which affects the accuracy and efficiency of prediction. In this paper, we present a coarse-to-fine feature-metric-based 6D object pose refinement framework, which utilizes the intermediate layers to没有准备 发表于 2025-3-27 05:34:04
http://reply.papertrans.cn/47/4694/469382/469382_33.png单独 发表于 2025-3-27 10:24:26
http://reply.papertrans.cn/47/4694/469382/469382_34.pnginvulnerable 发表于 2025-3-27 17:03:03
http://reply.papertrans.cn/47/4694/469382/469382_35.png偏见 发表于 2025-3-27 20:22:58
http://reply.papertrans.cn/47/4694/469382/469382_36.pngglacial 发表于 2025-3-28 00:08:24
Data Aggregation (DAgger) Algorithm Using Adversarial Agent Policy for Dynamic Situationsating diverse situations and does not account for improvements in the ego agent’s policy during training. To address these limitations, a method has been proposed that involves training by modeling the dynamic obstacle’s behavior as the adversarial agent policy. However, this method has been appliedEndemic 发表于 2025-3-28 02:20:48
http://reply.papertrans.cn/47/4694/469382/469382_38.png消耗 发表于 2025-3-28 06:52:43
http://reply.papertrans.cn/47/4694/469382/469382_39.png配偶 发表于 2025-3-28 12:03:08
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