Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise
CoRR(2024)
摘要
3D articulated objects are inherently challenging for manipulation due to the
varied geometries and intricate functionalities associated with articulated
objects.Point-level affordance, which predicts the per-point actionable score
and thus proposes the best point to interact with, has demonstrated excellent
performance and generalization capabilities in articulated object manipulation.
However, a significant challenge remains: while previous works use perfect
point cloud generated in simulation, the models cannot directly apply to the
noisy point cloud in the real-world.To tackle this challenge, we leverage the
property of real-world scanned point cloud that, the point cloud becomes less
noisy when the camera is closer to the object. Therefore, we propose a novel
coarse-to-fine affordance learning pipeline to mitigate the effect of point
cloud noise in two stages. In the first stage, we learn the affordance on the
noisy far point cloud which includes the whole object to propose the
approximated place to manipulate. Then, we move the camera in front of the
approximated place, scan a less noisy point cloud containing precise local
geometries for manipulation, and learn affordance on such point cloud to
propose fine-grained final actions. The proposed method is thoroughly evaluated
both using large-scale simulated noisy point clouds mimicking real-world scans,
and in the real world scenarios, with superiority over existing methods,
demonstrating the effectiveness in tackling the noisy real-world point cloud
problem.
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