PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
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. Experimental results show that our model outperforms the state-of-the-arts on both semantic segmentation and shape classification datasets.
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ScanNet
ModelNet
S3DIS
Results from the Paper
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
(Number of params metric)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Semantic Segmentation | S3DIS | PointWeb | Mean IoU | 66.7 | # 34 | |
| mAcc | 76.2 | # 22 | ||||
| oAcc | 87.3 | # 25 | ||||
| Number of params | N/A | # 1 | ||||
| Semantic Segmentation | S3DIS Area5 | PointWeb | oAcc | 87.0 | # 29 | |
| Number of params | N/A | # 2 |
