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Found 20 papers, 18 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.
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.
Learning to Match Features with Seeded Graph Matching Network
2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs.
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration.
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
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.
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.
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point.
Ranked #2 on
Point Cloud Registration
on KITTI
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 Two-View Correspondences and Geometry Using Order-Aware Network
First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix.
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.
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 #20 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
