33 citations · 72 across the 12 of their papers we have counts for
15 papers · 1 filter
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
Yu-Ting Yen, Chia-Ni Lu, Wei-Chen Chiu +1
For monocular depth estimation, acquiring ground truths for real data is not easy, and thus domain adaptation methods are commonly adopted using the supervised synthetic data. Howe…
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation
Fu-En Wang, Yu-Hsuan Yeh, Yi-Hsuan Tsai +2
Due to the rise of spherical cameras, monocular 360 depth estimation becomes an important technique for many applications (e.g., autonomous systems). Thus, state-of-the-art framewo…
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement
Meng-Shiun Tsai, Pei-Ze Chiang, Yi-Hsuan Tsai +1
Self-supervised learning on point clouds has gained a lot of attention recently, since it addresses the label-efficiency and domain-gap problems on point cloud tasks. In this paper…
RPG: Learning Recursive Point Cloud Generation
Wei-Jan Ko, Hui-Yu Huang, Yu-Liang Kuo +3
In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the tar…
Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images
Bolivar Solarte, Chin-Hsuan Wu, Kuan-Wei Lu +3
This paper presents a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e., equirectangular images)…
LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
Fu-En Wang, Yu-Hsuan Yeh, Min Sun +2
Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in t…