Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. In this paper, we impose multiple projection patterns into each single captured image to realize temporal super resolution of the depth image sequences. With our method, multiple patterns are projected onto the object with higher fps than possible with a camera. In this case, the observed pattern varies depending on the depth and motion of the object, so we can extract temporal information of the scene from each single image. The decoding process is realized using a learning-based approach where no geometric calibration is needed. Experiments confirm the effectiveness of our method where sequential shapes are reconstructed from a single image. Both quantitative evaluations and comparisons with recent techniques were also conducted.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectSuper-ResolutionSimilar Papers 제목 키워드 기반
ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration
Video restoration is a low-level vision task that seeks to restore clean, sharp videos from quality-degraded frames. One would use the temporal information from adjacent frames to make video restoration successful. Recen…
DeblurringRain RemovalVideo DeblurringVideo RestorationA Real-time and Registration-free Framework for Dynamic Shape Instantiation
Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisition speed and resolution, only 2D images…
AnatomyZooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
In this paper, we explore the space-time video super-resolution task, which aims to generate a high-resolution (HR) slow-motion video from a low frame rate (LFR), low-resolution (LR) video. A simple solution is to split …
Space-time Video Super-resolutionSuper-ResolutionVideo Frame InterpolationVideo Super-ResolutionSequential 3D Human Pose and Shape Estimation From Point Clouds
This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of d…
3D human pose and shape estimation3D Human Shape EstimationDiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency
Diffusion models have demonstrated exceptional capabilities in image generation and restoration, yet their application to video super-resolution faces significant challenges in maintaining both high fidelity and temporal…
DecoderImage GenerationSuper-ResolutionVideo Super-Resolution