Floorplan-Aware Camera Poses Refinement

Processing large indoor scenes is a challenging task, as scan registration and camera trajectory estimation methods accumulate errors across time. As a result, the quality of reconstructed scans is insufficient for some applications, such as visual-based localization and navigation, where the correct position of walls is crucial. For many indoor scenes, there exists an image of a technical floorplan that contains information about the geometry and main structural elements of the scene, such as walls, partitions, and doors. We argue that such a floorplan is a useful source of spatial information, which can guide a 3D model optimization. The standard RGB-D 3D reconstruction pipeline consists of a tracking module applied to an RGB-D sequence and a bundle adjustment (BA) module that takes the posed RGB-D sequence and corrects the camera poses to improve consistency. We propose a novel optimization algorithm expanding conventional BA that leverages the prior knowledge about the scene structure in the form of a floorplan. Our experiments on the Redwood dataset and our self-captured data demonstrate that utilizing floorplan improves accuracy of 3D reconstructions.

Pose-based Deep Gait Recognition

Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this paper, we present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, we consider the motion of points in the areas around human joints. To extract motion information, we estimate the optical flow between consecutive frames. We propose a deep convolutional model that computes pose-based gait descriptors. We compare different network architectures and aggregation methods and experimentally assess various sets of body parts to determine which are the most important for gait recognition. In addition, we investigate the generalization ability of the developed algorithms by transferring them between datasets. The results of these experiments show that our approach outperforms state-of-the-art methods.