Search Results for author:
Found 33 papers, 16 papers with code
Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching
Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks.
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
The multi-view 2. 5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation.
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e. g., depth maps).
NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images.
ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer
Generating robust and reliable correspondences across images is a fundamental task for a diversity of applications.
Critical Regularizations for Neural Surface Reconstruction in the Wild
The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input.
NeILF: Neural Incident Light Field for Physically-based Material Estimation
We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry.
Visibility-aware Multi-view Stereo Network
As such, the adverse influence of occluded pixels is suppressed in the cost fusion.
Ranked #1 on
Point Clouds
on DTU
Learning Stereo Matchability in Disparity Regression Networks
Finally, a matchability-aware disparity refinement is introduced to improve the depth inference in weakly matchable regions.
Ranked #2 on
Stereo Disparity Estimation
on KITTI 2015
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task.
Ranked #1 on
Video Object Segmentation
on FBMS
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction
In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability.
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
Recent learning-based approaches, in which models are trained by single-view images have shown promising results for monocular 3D face reconstruction, but they suffer from the ill-posed face pose and depth ambiguity issue.
Ranked #7 on
3D Face Reconstruction
on REALY (side-view)
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts
In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection.
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering
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.
ASLFeat: Learning Local Features of Accurate Shape and Localization
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures.
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
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 less on the ground-truth data.
Learning Fully Dense Neural Networks for Image Semantic Segmentation
On the other hand, it learns more efficiently with the more efficient gradient backpropagation.
Ranked #78 on
Semantic Segmentation
on NYU Depth v2
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.
Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes.
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM).
Ranked #3 on
Camera Pose Estimation
on KITTI Odometry Benchmark
Matchable Image Retrieval by Learning from Surface Reconstruction
Convolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction.
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction.
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space.
Reconstructing Thin Structures of Manifold Surfaces by Integrating Spatial Curves
We present a novel surface reconstruction method using both curves and point clouds.
Very Large-Scale Global SfM by Distributed Motion Averaging
This work proposes a divide-and-conquer framework to solve very large global SfM at the scale of millions of images.
MVSNet: Depth Inference for Unstructured Multi-view Stereo
We present an end-to-end deep learning architecture for depth map inference from multi-view images.
Ranked #19 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
Progressive Large Scale-Invariant Image Matching in Scale Space
In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views.
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus
In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation.
Parallel Structure from Motion from Local Increment to Global Averaging
In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel.
Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo
To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters.
Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces
In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments.
Local Readjustment for High-Resolution 3D Reconstruction
To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details.
