Segmenting humans in 3D indoor scenes has become increasingly important with the rise of human-centered robotics and AR/VR applications. In this direction, we explore the tasks of 3D human semantic-, instance- and multi-human body-part segmentation. Few works have attempted to directly segment humans in point clouds (or depth maps), which is largely due to the lack of training data on humans interacting with 3D scenes. We address this challenge and propose a framework for synthesizing virtual humans in realistic 3D scenes. Synthetic point cloud data is attractive since the domain gap between real and synthetic depth is small compared to images. Our analysis of different training schemes using a combination of synthetic and realistic data shows that synthetic data for pre-training improves performance in a wide variety of segmentation tasks and models. We further propose the first end-to-end model for 3D multi-human body-part segmentation, called Human3D, that performs all the above segmentation tasks in a unified manner. Remarkably, Human3D even outperforms previous task-specific state-of-the-art methods. Finally, we manually annotate humans in test scenes from EgoBody to compare the proposed training schemes and segmentation models.

Reliable and cost-effective counting of people in large indoor spaces is a significant challenge with many applications. An emerging approach is to deploy multiple fisheye cameras mounted overhead to monitor the whole space. However, due to the overlapping fields of view, person re-identificaiton (PRID) is critical for the accuracy of counting. While PRID has been thoroughly researched for traditional rectilinear cameras, few methods have been proposed for fisheye cameras and their performance is comparatively lower. To close this performance gap, we propose a multi-feature framework for fisheye PRID where we combine deep-learning, color-based and location-based features by means of novel feature fusion. We evaluate the performance of our framework for various feature combinations on FRIDA, a public fisheye PRID dataset. The results demonstrate that our multi-feature approach outperforms recent appearance-based deep-learning methods by almost 18% points and location-based methods by almost 3% points in accuracy.