activity
20182022
most citedSampling Neural Radiance Fields for Refractive Objects

13 citations · 27 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV202213 cited

Sampling Neural Radiance Fields for Refractive Objects

Jen-I Pan, Jheng-Wei Su, Kai-Wen Hsiao +2

Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing…

cs.CV2022

GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network

Jheng-Wei Su, Chi-Han Peng, Peter Wonka +1

Reconstructing 3D layouts from multiple panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a…

cs.CV20202 cited

Instance-aware Image Colorization

Jheng-Wei Su, Hung-Kuo Chu, Jia-Bin Huang

Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible col…

cs.GR202012 cited

Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video

Peng Wang, Lingjie Liu, Nenglun Chen +3

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digita…

cs.CV2019

Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods

Chuhang Zou, Jheng-Wei Su, Chi-Han Peng +5

Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step…

cs.CV2018

Self-Supervised Learning of Depth and Camera Motion from 360° Videos

Fu-En Wang, Hou-Ning Hu, Hsien-Tzu Cheng +5

As 360° cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360° perception becomes more and more important. We propose a novel self…