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Found 17 papers, 8 papers with code
Unbiased Estimator for Distorted Conics in Camera Calibration
Although conics are more informative features than points, the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration.
TSDF-Sampling: Efficient Sampling for Neural Surface Field using Truncated Signed Distance Field
Notably, our method is the first approach that can be robustly plug-and-play into a diverse array of neural surface field models, as long as they use the volume rendering technique.
Gramian Attention Heads are Strong yet Efficient Vision Learners
We introduce a novel architecture design that enhances expressiveness by incorporating multiple head classifiers (\ie, classification heads) instead of relying on channel expansion or additional building blocks.
X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary Correction
To overcome these limitations, we propose X-PDNet, a framework for the multitask learning of plane instance segmentation and depth estimation with improvements in the following two aspects.
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance.
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360 degrees coverage of stereo observations of the environment.
Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
In this paper, we introduce a collaborative training algorithm of balanced random forests with convolutional neural networks for domain adaptation tasks.
OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information.
ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems
For more robust and accurate ego-motion estimation we adds three components to the standard VO pipeline, 1) the hybrid projection model for improved feature matching, 2) multi-view P3P RANSAC algorithm for pose estimation, and 3) online update of rig extrinsic parameters.
SweepNet: Wide-baseline Omnidirectional Depth Estimation
Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind regions.
DFT-based Transformation Invariant Pooling Layer for Visual Classification
The proposed methods are extensively evaluated on various classification tasks using the ImageNet, CUB 2010-2011, MIT Indoors, Caltech 101, FMD and DTD datasets.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up.
Efficient Feature Matching by Progressive Candidate Search
We present a novel feature matching algorithm that systematically utilizes the geometric properties of features such as position, scale, and orientation, in addition to the conventional descriptor vectors.
Online multi-object tracking via robust collaborative model and sample selection
For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker.
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Hedged Deep Tracking
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking.
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
Online Object Tracking: A Benchmark
Object tracking is one of the most important components in numerous applications of computer vision.
