Depth image directly reflects the threedimensional geometric information of the scene surface and is not affected by factors such as light and shadow. Processing, recognizing, and understanding depth images are currently one of the hot topics and focuses in the field of threedimensional computer vision. Aiming at the problem that the depth image information is single and the noise is large, a threshold segmentation algorithm based on combined features is proposed to realize effective segmentation of depth image data. The algorithm first performs Otsu threshold segmentation on the image by using gradient features. On this basis, Otsu multithreshold segmentation is performed using depth features in different segmented regions to obtain candidate targets. Then, in the spatial domain, the depth feature is used to segment, merge, and denoise the candidate targets, thus finally obtaining the segmentation results. Experimental results show that this method can effectively overcome the influence of noise in depth images, the obtained boundary of the segmentation area is accurate, and the segmentation quality is high, which lays a good foundation for future indoor object recognition and scene understanding.