Mitigating Point Cloud Noise in Articulated Object Manipulation

Główne pojęcia

The author proposes a novel coarse-to-fine affordance learning pipeline to address the challenge of noisy point clouds in articulated object manipulation, leveraging the property that noise decreases with proximity. The approach involves two stages: learning affordance on noisy far point clouds and then refining it on less noisy local geometries.

Streszczenie

Articulated object manipulation is challenging due to varied geometries, but point-level affordance shows promise. The proposed method tackles real-world noisy point cloud issues by using a coarse-to-fine approach. It involves learning on noisy far point clouds first and then refining on less noisy local geometries for precise actions. The study demonstrates superiority over existing methods in simulated and real-world scenarios, showcasing effectiveness in handling noisy point cloud problems.
Key points:

3D articulated objects pose challenges for manipulation due to complex geometries.
Point-level affordance predicts actionable scores per point for effective manipulation.
Real-world scanned point clouds are noisier than perfect simulated ones.
A novel coarse-to-fine affordance learning pipeline is proposed to mitigate noise effects.
The method involves two stages: learning affordance on noisy far point clouds and refining it on less noisy local geometries.
Evaluation shows the method's effectiveness in both simulated and real-world scenarios.

Statystyki

"Large-scale simulated noisy point clouds"
"Real world scenarios"

Cytaty

"Our 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."
"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."

Kluczowe wnioski z

Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise

by Suhan Ling,Y... o arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18699.pdf

Głębsze pytania

How can this approach be adapted for other types of objects beyond articulated ones

This approach can be adapted for other types of objects beyond articulated ones by modifying the training data and task settings to suit the new object categories. The key lies in capturing the geometric information of the objects and their affordances, which can be achieved by adjusting the network architecture and training process. For different types of objects, such as deformable objects or rigid non-articulated objects, the network would need to learn specific features related to their manipulation requirements. By collecting diverse datasets that represent various object categories and interactions, the model can generalize better across different types of objects.

What potential limitations or drawbacks might arise from relying heavily on simulation-generated data

Relying heavily on simulation-generated data may introduce several limitations or drawbacks. One major limitation is the sim-to-real gap, where models trained on perfect simulated data may not perform well when deployed in real-world scenarios due to discrepancies between simulation and reality. This gap could lead to a lack of robustness in handling noise or variations present in real-world point cloud data. Additionally, over-reliance on simulated data may limit the model's adaptability to unforeseen challenges or novel environments that were not represented accurately in simulation.

How could advancements in camera technology impact the effectiveness of this method over time

Advancements in camera technology could significantly impact the effectiveness of this method over time by enhancing data acquisition capabilities. Higher-resolution cameras with improved depth sensing abilities can provide more detailed and accurate point cloud representations, leading to better affordance predictions for manipulation tasks. Additionally, advancements like increased frame rates or wider field-of-view cameras could enable faster processing speeds and broader coverage during interaction tasks. These improvements would enhance both training efficiency with higher-quality input data and real-time performance during actual robot manipulations using this method.