Going Further with Point Pair Features
This work addresses a specific issue in 3D object detection for robotics or computer vision applications, representing an incremental improvement to an existing method.
The paper tackled the problem of Point Pair Features (PPF) failing due to sensor noise and background clutter in 3D object detection by introducing novel sampling and voting schemes, resulting in competitive performance that outperforms state-of-the-art methods on several objects from challenging benchmarks at low computational cost.
Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.