【论文笔记】PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

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已于 2022-04-09 12:08:17 修改 · 1k 阅读

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#深度学习 #python

于 2022-04-08 22:54:00 首次发布

本文提出PointWeb方法，通过密集连接点云邻域并引入Adaptive Feature Adjustment (AFA)模块，以捕捉点对间交互，增强区域特征表示。AFA利用MLP学习权重，结合点特征差异，调整区域特征，显著提升语义分割和形状分类任务表现。

1. 摘要部分：本文的贡献+实验结果

本文的贡献

This paper presents PointWeb, a new approach to extract contextual features from local neighborhood in a point cloud. Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region.
A novel module, namely Adaptive Feature Adjustment (AFA) module, is presented to find the interaction between points. For each local region, an impact map carrying element-wise impact between point pairs is applied to the feature difference map.
Each feature is then pulled or pushed by other features in the same region according to the adaptively learned impact indicators. The adjusted features are well encoded with region information, and thus benefit the point cloud recognition tasks, such as point cloud segmentation and classification.

实验结果

Experimental results show that our model outperforms the state-of-the-arts on both semantic segmentation and shape classification datasets.

2. 论文采用的AFA模块方法

AFA模块通过利用每个点对之间关系来加强训练效果，两句话总结AFA干了什么：

AFA就是在原point features上+局部的上下文信息
这个局部的上下文信息是Impact Function 和Relation Function 的乘积求和。Impact Function代表将两个point features间关系通过MLP求出的weight，Relation Function代表两个point features间的关系，这两个point features间关系实际上的实现都是两point feature求差。

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本文的总体目标：

Exploring the relationship among points in a local region is the focus of this paper. Particularly, after we extract
pointwise features (or point features, for short) using a neural network, further aggregating these local features helps
improve the point cloud recognition quality for the tasks of semantic segmentation and classification.

Adaptive Feature Adjustment (AFA) Module

Given region R and its feature set
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we first formulate the adaptive feature adjustment (AFA) module to enhance the point features in F by learning the contextual information in local neighborhood as

Impact Function fimp

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We use MLP to calculate the impact function fimp, as illustrated in Fig. 3. It is formulated as

Relation Function frel

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3.AFA部分关键代码段

class _AFAModule(nn.Module):
    def __init__(self, mlp, use_softmax=False):
        r"""
        :param mlp: mlp for learning weight
               mode: transformation or aggregation
        """
        super().__init__()
        self.mlp = mlp
        self.use_softmax = use_softmax

    def forward(self, feature: torch.Tensor) -> torch.Tensor:
        r"""
        Parameters
        ----------
        features : torch.Tensor
            (B, C, N, M) or (B, C, N)
        Returns
        -------
        new_features : torch.Tensor
            transformation: (B, C, N, M) or (B, C, N)
            aggregation: (B, C, N) or (B, C)
        """
        B, C, N, M = feature.siz