PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling (2003.00492v3)

Abstract: Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise. Our code is released through https://github.com/yanx27/PointASNL.

PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks with Adaptive Sampling

In the domain of 3D point cloud data processing, raw data acquired from 3D sensors or reconstruction algorithms often contain noise or outliers. The paper "PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling" introduces an innovative approach aimed at addressing these challenges effectively. The proposed end-to-end network, PointASNL, leverages adaptive sampling and local-nonlocal modules to enhance the robustness and performance of point cloud processing tasks, particularly classification and segmentation.

Key Components and Methodology

The PointASNL framework comprises two critical modules:

1. Adaptive Sampling (AS) Module: This module is designed to adjust the coordinates of sampled points to better fit intrinsic geometry while mitigating the impact of outliers. Initially, farthest point sampling (FPS) is employed to select points, which are subsequently refined through adaptive adjustments. These adjustments leverage self-attention mechanisms to update local group features and recalibrate point importance, thereby improving feature learning and noise resilience.
2. Local-Nonlocal (L-NL) Module: To address both neighbor and long-range dependencies, the L-NL module combines a point local (PL) cell with a point nonlocal (PNL) cell. The PL cell focuses on local feature aggregation using convolution-style operations, while the PNL cell enhances global context learning by considering entire point cloud information for each sampled point. This dual approach ensures that the learning process remains insensitive to noise, facilitating robust feature extraction from both local and global perspectives.

The methodology emphasizes a hierarchical architecture where these modules are integrated to effectively process complex point clouds, resulting in superior performance across different types of datasets, including synthetic, indoor, and large-scale outdoor scenarios.

Experimental Evaluation

The effectiveness of PointASNL is demonstrated through extensive experiments on multiple datasets:

• ModelNet40 and ModelNet10: For shape classification, PointASNL exhibits strong numerical results, achieving top-tier accuracy compared to state-of-the-art methods. Notably, the model maintains robustness despite the presence of noise, outperforming other leading approaches under challenging conditions.
• S3DIS and ScanNet: In indoor semantic segmentation tasks, PointASNL significantly improves performance measured by mean Intersection over Union (mIoU), leveraging its ability to capture both local and global features effectively.
• SemanticKITTI: In outdoor scene segmentation, PointASNL achieves notable improvements over existing methods, demonstrating its robustness and scalability in real-world, noise-prone environments.

Implications and Future Directions

The proposed PointASNL architecture presents both practical and theoretical advancements in point cloud processing:

• Practical Implications: The adaptive sampling approach allows for more efficient handling of noisy data, making PointASNL particularly beneficial for applications such as autonomous driving and robotics, where 3D data integrity is crucial.
• Theoretical Implications: The integration of local and nonlocal operations in a single framework offers a promising direction for future research on feature learning in unstructured data contexts.

Looking ahead, further refinement of adaptive sampling strategies and exploration of deeper nonlocal dependencies could enhance the scalability and versatility of neural networks processing point cloud data. Continued research may also focus on optimizing computational efficiency to enable real-time applications on edge devices.

In conclusion, the paper presents a coherent and robust framework for point cloud processing, pushing the boundaries of current methodologies through adaptive and nonlocal neural network operations.