1 citations · 2 across the 15 of their papers we have counts for
19 papers
HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM
Nam Tran, Thu Tran, Hieu Phan +4
Achieving robustness, accuracy, and efficiency simultaneously remains a central challenge in light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM). While…
Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios
Wongi Park, Jiyeon Lim, Minjae Lee +6
We present RefineSplat, a systematic framework that effectively constructs transient masks to identify diverse ambiguous distractors. To do this, we qualitatively and quantitativel…
Simulation-Based Multi-Fillet Evaluation of Woody Breast Poultry Fillets
Chirantan Sen Mukherjee, Seung-Chul Yoon, William J. Beksi
Woody breast (WB) is a myopathy in modern broiler chickens that causes the breast muscle to become unusually stiff and fibrous, leading to decreased meat quality and significant ec…
Energy-Based Open-Set Active Learning for Object Classification
Zongyao Lyu, William J. Beksi
Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a pool of unlabeled data for an…
Difix3D-W: Distractor-Free Few-Shot 3D Gaussian Splatting in the Wild
Wongi Park, Jordan A. James, Myeongseok Nam +4
We propose Difix3D-W, a 3D novel sparse-view synthesis framework for unconstrained real-world scenarios that contain distractors, occlusion, and appearance variation. Unlike existi…
CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting
Md Ahmed Al Muzaddid, William J. Beksi
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined wi…
CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
Md Ahmed Al Muzaddid, Jordan A. James, William J. Beksi
Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequ…
Person Re-Identification via Generalized Class Prototypes
Md Ahmed Al Muzaddid, William J. Beksi
Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been de…
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting
Md Ahmed Al Muzaddid, William J. Beksi
In agriculture, automating the accurate tracking of fruits, vegetables, and fiber is a very tough problem. The issue becomes extremely challenging in dynamic field environments. Ye…
IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstruction
Mohammad Samiul Arshad, William J. Beksi
Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an underexplored area of computer vision. Recent learning-based implicit techniques have removed previous barrie…
LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction
Mohammad Samiul Arshad, William J. Beksi
Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/i…
Single Image Super-Resolution via a Dual Interactive Implicit Neural Network
Quan H. Nguyen, William J. Beksi
In this paper, we introduce a novel implicit neural network for the task of single image super-resolution at arbitrary scale factors. To do this, we represent an image as a decodin…
Automated Reconstruction of 3D Open Surfaces from Sparse Point Clouds
Mohammad Samiul Arshad, William J. Beksi
Real-world 3D data may contain intricate details defined by salient surface gaps. Automated reconstruction of these open surfaces (e.g., non-watertight meshes) is a challenging pro…
Variable Rate Compression for Raw 3D Point Clouds
Md Ahmed Al Muzaddid, William J. Beksi
In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression metho…
An Uncertainty Estimation Framework for Probabilistic Object Detection
Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
In this paper, we introduce a new technique that combines two popular methods to estimate uncertainty in object detection. Quantifying uncertainty is critical in real-world robotic…
Vision-Based Guidance for Tracking Dynamic Objects
Pritam Karmokar, Kashish Dhal, William J. Beksi +1
In this paper, we present a novel vision-based framework for tracking dynamic objects using guidance laws based on a rendezvous cone approach. These guidance laws enable an unmanne…
Learning the Next Best View for 3D Point Clouds via Topological Features
Christopher Collander, William J. Beksi, Manfred Huber
In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The me…
A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds
Mohammad Samiul Arshad, William J. Beksi
In this paper, we introduce a novel conditional generative adversarial network that creates dense 3D point clouds, with color, for assorted classes of objects in an unsupervised ma…
Camera-Based Adaptive Trajectory Guidance via Neural Networks
Aditya Rajguru, Christopher Collander, William J. Beksi
In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing…