6 citations · 11 across the 2 of their papers we have counts for
5 papers
Modular Framework for Visuomotor Language Grounding
Kolby Nottingham, Litian Liang, Daeyun Shin +3
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end…
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation
Zhe Wang, Daeyun Shin, Charless C. Fowlkes
Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data…
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation
Yunhan Zhao, Shu Kong, Daeyun Shin +1
Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap…
3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers
Daeyun Shin, Zhile Ren, Erik B. Sudderth +1
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and…
Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction
Daeyun Shin, Charless C. Fowlkes, Derek Hoiem
The goal of this paper is to compare surface-based and volumetric 3D object shape representations, as well as viewer-centered and object-centered reference frames for single-view 3…