31 citations · 72 across the 11 of their papers we have counts for
11 papers
PointConvFormer: Revenge of the Point-based Convolution
Wenxuan Wu, Li Fuxin, Qi Shan
We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point…
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…
Fast and Explicit Neural View Synthesis
Pengsheng Guo, Miguel Angel Bautista, Alex Colburn +4
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects. We propose a simple yet effective approach that is neither continuo…
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions
Zhenpei Yang, Zhile Ren, Qi Shan +1
Deep learning has made significant impacts on multi-view stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution o…
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…
RIDI: Robust IMU Double Integration
Hang Yan, Qi Shan, Yasutaka Furukawa
This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU…
Panoramic Structure from Motion via Geometric Relationship Detection
Satoshi Ikehata, Ivaylo Boyadzhiev, Qi Shan +1
This paper addresses the problem of Structure from Motion (SfM) for indoor panoramic image streams, extremely challenging even for the state-of-the-art due to the lack of textures…