19 citations · 35 across the 5 of their papers we have counts for
9 papers
Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization
Rogan Morrow, Wei-Chen Chiu
There exist many forms of deep latent variable models, such as the variational autoencoder and adversarial autoencoder. Regardless of the specific class of model, there exists an i…
Variational Autoencoders with Normalizing Flow Decoders
Rogan Morrow, Wei-Chen Chiu
Recently proposed normalizing flow models such as Glow have been shown to be able to generate high quality, high dimensional images with relatively fast sampling speed. Due to thei…
LayoutMP3D: Layout Annotation of Matterport3D
Fu-En Wang, Yu-Hsuan Yeh, Min Sun +2
Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and nav…
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Ning-Hsu Wang, Bolivar Solarte, Yi-Hsuan Tsai +2
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images. However, 360° images captured under equirectangular…
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
Hsueh-Ying Lai, Yi-Hsuan Tsai, Wei-Chen Chiu
Stereo matching and flow estimation are two essential tasks for scene understanding, spatially in 3D and temporally in motion. Existing approaches have been focused on the unsuperv…
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
Tsun-Hsuan Wang, Hou-Ning Hu, Chieh Hubert Lin +3
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception. Instead o…