Depth segmentation has the challenge of separating the objects from their supporting surfaces in a noisy environment. To address the issue, a novel segmentation scheme based on disparity analysis is proposed. First, we transform a depth scene into the corresponding U-V disparity map. Then, we conduct a region-based detection method to divide the object region into several targets in the processed U-disparity map. Thirdly, the horizontal plane regions may be mapped as slant lines in the V-disparity map, the Random Sample Consensus (RANSAC) algorithm is improved to fit such multiple lines. Moreover, noise regions are reduced by image processing strategies during the above processes. We respectively evaluate our approach on both real-world scenes and public data sets to verify the flexibility and generalization. Sufficient experimental results indicate that the algorithm can efficiently segment and label a full-view scene into a group of valid regions as well as removing surrounding noise regions.

深度分割具有在嘈杂的环境中将对象从其支撑表面分离的挑战。为了解决这个问题，提出了一种基于视差分析的新颖分割方案。首先，我们将深度场景转换为相应的UV视差图。然后，我们执行基于区域的检测方法，将目标区域在已处理的U视差图中划分为多个目标。第三，可以将水平面区域映射为V视差图中的倾斜线，改进了随机采样共识（RANSAC）算法以适合这种多条线。此外，在上述过程中通过图像处理策略减少了噪声区域。我们分别在现实场景和公共数据集上评估我们的方法，以验证灵活性和概括性。