Search Results for author:
Found 21 papers, 12 papers with code
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
The advent of wearable computers enables a new source of context for AI that is embedded in egocentric sensor data.
Ranked #1 on
3D Reconstruction
on Aria Synthetic Environments
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism.
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
This poses a potential risk to user privacy.
Domain Adaptation of Learned Features for Visual Localization
We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons.
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.
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.
Ranked #1 on
Edge Detection
on Cityscapes test
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.
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.
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.
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.
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.
