Domain Reduction Strategy for Non Line of Sight Imaging

This paper presents a novel optimization-based method for non-line-of-sight (NLOS) imaging that aims to reconstruct hidden scenes under various setups. Our method is built upon the observation that photons returning from each point in hidden volumes can be independently computed if the interactions between hidden surfaces are trivially ignored. We model the generalized light propagation function to accurately represent the transients as a linear combination of these functions. Moreover, our proposed method includes a domain reduction procedure to exclude empty areas of the hidden volumes from the set of propagation functions, thereby improving computational efficiency of the optimization. We demonstrate the effectiveness of the method in various NLOS scenarios, including non-planar relay wall, sparse scanning patterns, confocal and non-confocal, and surface geometry reconstruction. Experiments conducted on both synthetic and real-world data clearly support the superiority and the efficiency of the proposed method in general NLOS scenarios.

Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction

We propose a deep video prediction model conditioned on a single image and an action class. To generate future frames, we first detect keypoints of a moving object and predict future motion as a sequence of keypoints. The input image is then translated following the predicted keypoints sequence to compose future frames. Detecting the keypoints is central to our algorithm, and our method is trained to detect the keypoints of arbitrary objects in an unsupervised manner. Moreover, the detected keypoints of the original videos are used as pseudo-labels to learn the motion of objects. Experimental results show that our method is successfully applied to various datasets without the cost of labeling keypoints in videos. The detected keypoints are similar to human-annotated labels, and prediction results are more realistic compared to the previous methods.