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0 events,
3 events,
VASC Seminar
Qitao Zhao
Computer Vision, Carnegie Mellon University
Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
Abstract: This talk will present our approach for reconstructing objects from sparse-view images captured in unconstrained environments. In the absence of ground-truth camera poses, we will demonstrate how to utilize estimates from off-the-shelf systems and address two key challenges: refining noisy camera poses in sparse views and effectively handling outlier poses. Bio: Qitao is a second-year […]
VASC Seminar
Vimal Mollyn
Human Computer Interaction Institute, Carnegie Mellon University
EgoTouch: On-Body Touch Input Using AR/VR Headset Cameras
Abstract: In augmented and virtual reality (AR/VR) experiences, a user’s arms and hands can provide a convenient and tactile surface for touch input. Prior work has shown on-body input to have significant speed, accuracy, and ergonomic benefits over in-air interfaces, which are common today. In this work, we demonstrate high accuracy, bare hands (i.e., no special […]
VASC Seminar
Hyunsung Cho
Human-Computer Interaction Institute (HCII) , Carnegie Mellon University
Auptimize: Optimal Placement of Spatial Audio Cues for Extended Reality
Abstract: Spatial audio in Extended Reality (XR) provides users with better awareness of where virtual elements are placed, and efficiently guides them to events such as notifications, system alerts from different windows, or approaching avatars. Humans, however, are inaccurate in localizing sound cues, especially with multiple sources due to limitations in human auditory perception such as […]
2 events,
MSR Thesis Defense
VoxDet: Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel […]
MSR Thesis Defense
Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel […]