5 accepted papers
Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning c…
For high-level human-robot interaction tasks, 3D scene understanding is important and non-trivial for autonomous robots. However, parsing and utilizing effective environment information of the 3D scene is not trivial due to the complexity of the 3D environment and the limited ability for reasoning a…
Dense mapping is always the desire of simultaneous localization and mapping (SLAM), especially for the applications that require fast and dense scene information. Visual-inertial odometry (VIO) is a light-weight and effective solution to fast self-localization. However, VIO-based SLAM systems have d…
Random sample consensus (RANSAC) is a popular paradigm for parameter estimation with outlier detection, which plays an essential role in 3D robot vision, especially for LiDAR odometry. The success of RANSAC strongly depends on the probability of selecting a subset of pure inliers, which sets barrier…
The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and lim…