Feature Preserving and Uniformity-controllable Point Cloud Simplification on Graph
This work addresses storage and computation issues in 3D applications like autonomous driving and heritage reconstruction, but it is incremental as it builds on existing graph-based methods.
The paper tackles the challenge of processing large-scale point clouds by proposing a simplification algorithm that balances preserving sharp features and maintaining uniform density, achieving superior results and efficient application in point cloud registration.
With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction. However, it is challenging to process large-scale point clouds in terms of both computation time and storage due to the tremendous amounts of data. Hence, we propose a point cloud simplification algorithm, aiming to strike a balance between preserving sharp features and keeping uniform density during resampling. In particular, leveraging on graph spectral processing, we represent irregular point clouds naturally on graphs, and propose concise formulations of feature preservation and density uniformity based on graph filters. The problem of point cloud simplification is finally formulated as a trade-off between the two factors and efficiently solved by our proposed algorithm. Experimental results demonstrate the superiority of our method, as well as its efficient application in point cloud registration.