Search Results for author:
Found 7 papers, 4 papers with code
360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception
Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results as the compression process often muddles the semantics between various planes.
Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness
Based on the Manhattan World assumption, most existing indoor layout estimation schemes focus on recovering layouts from vertically compressed 1D sequences.
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech Recognition
In this paper, we propose a language-universal adapter learning framework based on a pre-trained model for end-to-end multilingual automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Neural Contourlet Network for Monocular 360 Depth Estimation
For a monocular 360 image, depth estimation is a challenging because the distortion increases along the latitude.
Ranked #8 on
Depth Estimation
on Stanford2D3D Panoramic
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation
In this paper, we combine the two different representations and propose a novel 360{\deg} semantic segmentation solution from a complementary perspective.
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap
It comprises two modules: Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module.
PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation
In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions.
Ranked #4 on
Depth Estimation
on Stanford2D3D Panoramic
