Search Results for author:
Found 27 papers, 14 papers with code
GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D Object Pose Estimation
At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method.
Ray Launching-Based Computation of Exact Paths with Noisy Dense Point Clouds
It is observed that the proposed method is capable of adapting to noise and finds similar paths compared to the baseline path trajectories with noisy point clouds.
A Ray Launching Approach for Computing Exact Paths with Point Clouds
Ray tracing is a deterministic method that produces propagation paths between a transmitter and a receiver.
Unbiased Scene Graph Generation via Two-stage Causal Modeling
To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages.
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images.
MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation
Though massive amounts of synthetic RGB images are easy to obtain, the models trained on them suffer from noticeable performance degradation due to the synthetic-to-real domain gap.
Sparse resultant based minimal solvers in computer vision and their connection with the action matrix
Most state-of-the-art efficient polynomial solvers are based on the action matrix method that has been automated and highly optimized in recent years.
BS3D: Building-scale 3D Reconstruction from RGB-D Images
Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems.
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scale-invariant distance estimation (the translation along the z-axis) via classification.
Optimal Correction Cost for Object Detection Evaluation
OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy.
OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation
This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask.
Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
In this paper, we present a series of experiments assessing how well the benchmark results reflect the true progress in solving the moment retrieval task.
Computing stable resultant-based minimal solvers by hiding a variable
The existing state-of-the-art methods for solving such systems are either based on Gr\"obner bases and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a sparse resultant computation using an extra variable.
Learning non-rigid surface reconstruction from spatio-temporal image patches
We present a method to reconstruct a dense spatio-temporal depth map of a non-rigidly deformable object directly from a video sequence.
A sparse resultant based method for efficient minimal solvers
Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gr\"obner basis methods for minimal problems in computer vision.
Improving land cover segmentation across satellites using domain adaptation
We applied a well-performing domain adaptation approach on datasets we have built using RGB images from Sentinel-2, WorldView-2, and Pleiades-1 satellites with Corine Land Cover as ground-truth labels.
Rethinking the Evaluation of Video Summaries
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content.
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions
Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding.
LSD$_2$ -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs
The paper addresses the problem of acquiring high-quality photographs with handheld smartphone cameras in low-light imaging conditions.
Gyroscope-Aided Motion Deblurring with Deep Networks
We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN).
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs.
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors.
Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement
Then, the original depth maps are re-registered to the fused point cloud to refine the original camera extrinsic parameters.
Cell Tracking via Proposal Generation and Selection
Microscopy imaging plays a vital role in understanding many biological processes in development and disease.
Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices
In the process, we also perform a temporal and spatial alignment of the camera and the IMU.
Video Summarization using Deep Semantic Features
For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions.
Learning Joint Representations of Videos and Sentences with Web Image Search
In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.
