2 citations · 4 across the 3 of their papers we have counts for
7 papers
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
Zhenpei Yang, Zhile Ren, Miguel Angel Bautista +3
Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate cam…
Texturify: Generating Textures on 3D Shape Surfaces
Yawar Siddiqui, Justus Thies, Fangchang Ma +3
Texture cues on 3D objects are key to compelling visual representations, with the possibility to create high visual fidelity with inherent spatial consistency across different view…
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
Yawar Siddiqui, Justus Thies, Fangchang Ma +3
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to tra…
Equivariant Neural Rendering
Emilien Dupont, Miguel Angel Bautista, Alex Colburn +4
We propose a framework for learning neural scene representations directly from images, without 3D supervision. Our key insight is that 3D structure can be imposed by ensuring that…
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…
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Chuhang Zou, Alex Colburn, Qi Shan +1
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. L-shape r…