1 citations · 2 across the 3 of their papers we have counts for
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UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
Shuai Yuan, Lei Luo, Zhuo Hui +4
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAMFlow, an unsupe…
UDFNet: Unsupervised Disparity Fusion with Adversarial Networks
Can Pu, Robert B. Fisher
Existing disparity fusion methods based on deep learning achieve state-of-the-art performance, but they require ground truth disparity data to train. As far as I know, this is the…
DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment
Can Pu, Nanbo Li, Radim Tylecek +1
Registering accurately point clouds from a cheap low-resolution sensor is a challenging task. Existing rigid registration methods failed to use the physical 3D uncertainty distribu…
Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks
Can Pu, Runzi Song, Radim Tylecek +2
Refining raw disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships amo…
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
Can Pu, Nanbo Li, Robert B Fisher
Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining…