# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 2 Related Work
### RGB-Depth Superpixel Algorithms  
For the sake of clarity, we should emphasize that the RGBD superpixel algorithms are not related to existing supervoxel methods [27], which are simple extensions of 2D algorithms to 3D volumes. In such methods, video frames are stacked to produce a structured, regular, and solid volume, with time as the depth dimension. In contrast, the RGBD superpixel algorithms are intended to segment actual volumes in space and make heavy use of the fact that such volumes are not regular or solid (most of the volume is empty space) to aid segmentation. Existing supervoxel methods cannot work in such a space, as they generally only function on structured lattices.  
Depth-adaptive superpixels (DASP) [28] is a clustering-based superpixel algorithm for RGBD images. In contrast to previous approaches, it uses 3D information in addition to color. With depth-adaptive superpixels, previously costly segmentation methods are available to real-time scenarios, opening the possibility for a variety of new applications.  
However, DASP has no advantage in terms of accuracy and speed compared with the other RGBD superpixel algorithms. Although DASP has a theoretical linear time complexity of \(O(N)\), it initializes seeds adaptively to depth with blue-noise spectrum property. The process of drawing blue-noise samples is quite time consuming; it thus entails a higher time cost for initializing the seeds, especially when a large number of seeds are required. Meanwhile, DASP excessively considers the reduction of undersegmentation errors, mentioned in [24], so its accuracy in terms of boundary call is lower than other RGBD superpixel algorithms.  
Another clustering-based algorithm, voxel cloud connectivity segmentation (VCCS) [29] takes advantage of the 3D geometry provided by RGBD cameras to generate superpixels that are evenly distributed in the actual observed space as opposed to the projected image plane. This is accomplished using a seeding methodology based in 3D space as well as a flow-constrained local iterative clustering that uses color and geometric features. In this case, VCCS has a high computational cost with \(O(N\cdot K)\) time complexity for an \(N\)-pixel image, where \(K\) is the desired number of superpixels. In addition to providing superpixels that conform to real geometric relationships, the method can also be used directly on point clouds created by combining several calibrated RGBD cameras, providing a full 3D supervoxel graph. Furthermore, the source code of VCCS uses a multi-threading framework, and the speed is sufficient for robotic applications.  
However, VCCS performs poorly in large scene images and prefers to produce superpixels with irregular sizes and shapes. In many indoor environments, there may be many objects in just one RGBD image captured by depth sensors, and the scene is usually cluttered. We call this type of RGBD picture a large scene image. Since VCCS considers a lot of space information, it performs poorly in cluttered, large scenes. Moreover, VCCS chooses a multi-dimensional depth feature of the point cloud. Therefore, the shapes and sizes of generated superpixels are irregular, which may cause trouble in the post-processing process.