TC-SfM: Robust Track-Community-Based Structure-From-Motion.

Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures.

CoLi-BA: Compact Linearization based Solver for Bundle Adjustment.

Bundle adjustment (BA) is widely used in SLAM and SfM, which are key technologies in Augmented Reality. For real-time SLAM and large-scale SfM, the efficiency of BA is of great importance. This paper proposes CoLi-BA, a novel and efficient BA solver that significantly improves the optimization speed by compact linearization and reordering.

Physics-Guided Reflection Separation From a Pair of Unpolarized and Polarized Images.

Undesirable reflections contained in photos taken in front of glass windows or doors often degrade visual quality of the image. Separating two layers apart benefits both human and machine perception. The polarization status of the light changes after refraction or reflection, providing more observations of the scene, which can benefit the reflection separation.

Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments.

3D hand pose estimation is a challenging problem in computer vision due to the high degrees-of-freedom of hand articulated motion space and large viewpoint variation. As a consequence, similar poses observed from multiple views can be dramatically different. In order to deal with this issue, view-independent features are required to achieve state-of-the-art performance.

Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling.

We present a multi-sensor system for consistent 3D hand pose tracking and modeling that leverages the advantages of both wearable and optical sensors. Specifically, we employ a stretch-sensing soft glove and three IMUs in combination with an RGB-D camera. Different sensor modalities are fused based on the availability and confidence estimation, enabling seamless hand tracking in challenging environments with partial or even complete occlusion.

Hand Pose Understanding With Large-Scale Photo-Realistic Rendering Dataset.

Hand pose understanding is essential to applications such as human computer interaction and augmented reality. Recently, deep learning based methods achieve great progress in this problem. However, the lack of high-quality and large-scale dataset prevents the further improvement of hand pose related tasks such as 2D/3D hand pose from color and depth from color.

Weakly Supervised Learning for Single Depth-Based Hand Shape Recovery.

Recent emerging technologies such AR/VR and HCI are drawing high demand on more comprehensive hand shape understanding, requiring not only 3D hand skeleton pose but also hand shape geometry. In this paper, we propose a deep learning framework to produce 3D hand shape from a single depth image. To address the challenge that capturing ground truth 3D hand shape in the training dataset is non-trivial, we leverage synthetic data to construct a statistical hand shape model and adopt weak supervision from widely accessible hand skeleton pose annotation.