201 citations · 414 across the 18 of their papers we have counts for
18 papers
Matrix3D: Large Photogrammetry Model All-in-One
Yuanxun Lu, Jingyang Zhang, Tian Fang +6
We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model.…
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
Yuanxun Lu, Jingyang Zhang, Shiwei Li +6
Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the…
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
Jingyang Zhang, Shiwei Li, Yuanxun Lu +5
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended f…
NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation
Jingyang Zhang, Yao Yao, Shiwei Li +5
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a s…
Critical Regularizations for Neural Surface Reconstruction in the Wild
Jingyang Zhang, Yao Yao, Shiwei Li +4
Neural implicit functions have recently shown promising results on surface reconstructions from multiple views. However, current methods still suffer from excessive time complexity…
NeILF: Neural Incident Light Field for Physically-based Material Estimation
Yao Yao, Jingyang Zhang, Jingbo Liu +5
We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene light…
Learning Signed Distance Field for Multi-view Surface Reconstruction
Jingyang Zhang, Yao Yao, Long Quan
Recent works on implicit neural representations have shown promising results for multi-view surface reconstruction. However, most approaches are limited to relatively simple geomet…
Visibility-aware Multi-view Stereo Network
Jingyang Zhang, Yao Yao, Shiwei Li +2
Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration…
Learning Stereo Matchability in Disparity Regression Networks
Jingyang Zhang, Yao Yao, Zixin Luo +4
Learning-based stereo matching has recently achieved promising results, yet still suffers difficulties in establishing reliable matches in weakly matchable regions that are texture…
ASLFeat: Learning Local Features of Accurate Shape and Localization
Zixin Luo, Lei Zhou, Xuyang Bai +6
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors. First, the ability to estimate the local shape (scale, orientation…
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering
Lei Zhou, Zixin Luo, Tianwei Shen +5
Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though th…
Network Cooperation with Progressive Disambiguation for Partial Label Learning
Yao Yao, Chen Gong, Jiehui Deng +1
Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessi…
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
Yao Yao, Zixin Luo, Shiwei Li +5
While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compar…
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
Tianwei Shen, Lei Zhou, Zixin Luo +5
The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much les…
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
Zixin Luo, Tianwei Shen, Lei Zhou +5
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keyp…
Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
Yao Yao, Zixin Luo, Shiwei Li +3
Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: t…
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
Zixin Luo, Tianwei Shen, Lei Zhou +5
Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong gen…
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao Yao, Zixin Luo, Shiwei Li +2
We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the…