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