4 citations · 10 across the 18 of their papers we have counts for
23 papers
Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense
Fernando Martinez, Abhishek Satyam, Tao Li +3
Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient in settings where defenders m…
VCURF: Virtual Camera-based Uncertainty of Radiance Fields
Liyan Chen, Nathaniel Burgdorfer, Philippos Mordohai
Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pac…
TV-SGS: Gaussian Splatting with Geometric Information Propagation via Tensor Voting under sparse views
Harish N Sathishchandra, Philippos Mordohai
Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to synthesize the pixels of novel…
Mapping Pamir: Multi-Session Visual-Inertial SLAM and 3D Reconstruction of an Underwater Shipwreck
Michalis Chatzispyrou, Luke Horgan, Hyunkil Hwang +6
This paper presents a framework for multi-session mapping of underwater environments utilizing an affordable action camera. The Visual-Inertial data are augmented by water depth re…
Is Image-based Object Pose Estimation Ready to Support Grasping?
Eric C. Joyce, Qianwen Zhao, Nathaniel Burgdorfer +2
We present a framework for evaluating 6-DoF instance-level object pose estimators, focusing on those that require a single RGB (not RGB-D) image as input. Besides gaining intuition…
Mapping the Catacombs: An Underwater Cave Segment of the Devil's Eye System
Michalis Chatzispyrou, Luke Horgan, Hyunkil Hwang +5
This paper presents a framework for mapping underwater caves. Underwater caves are crucial for fresh water resource management, underwater archaeology, and hydrogeology. Mapping th…
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Eric C. Joyce, Qianwen Zhao, Nathaniel Burgdorfer +2
Deep object pose estimators are notoriously overconfident. A grasping agent that both estimates the 6-DoF pose of a target object and predicts the uncertainty of its own estimate c…
Understanding while Exploring: Semantics-driven Active Mapping
Liyan Chen, Huangying Zhan, Hairong Yin +2
Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an a…
Radiant Triangle Soup with Soft Connectivity Forces for 3D Reconstruction and Novel View Synthesis
Nathaniel Burgdorfer, Philippos Mordohai
We introduce an inference-time scene optimization algorithm utilizing triangle soup, a collection of disconnected translucent triangle primitives, as the representation for the geo…
Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction
Bo Sun, Hao Kang, Li Guan +3
We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. P…
Stereo-NEC: Enhancing Stereo Visual-Inertial SLAM Initialization with Normal Epipolar Constraints
Weihan Wang, Chieh Chou, Ganesh Sevagamoorthy +7
We propose an accurate and robust initialization approach for stereo visual-inertial SLAM systems. Unlike the current state-of-the-art method, which heavily relies on the accuracy…
V-FUSE: Volumetric Depth Map Fusion with Long-Range Constraints
Nathaniel Burgdorfer, Philippos Mordohai
We introduce a learning-based depth map fusion framework that accepts a set of depth and confidence maps generated by a Multi-View Stereo (MVS) algorithm as input and improves them…
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Weihan Wang, Jiani Li, Yuhang Ming +1
Visual-inertial initialization can be classified into joint and disjoint approaches. Joint approaches tackle both the visual and the inertial parameters together by aligning observ…
Real-Time Dense 3D Mapping of Underwater Environments
Weihan Wang, Bharat Joshi, Nathaniel Burgdorfer +4
This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challe…
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation
Liyan Chen, Weihan Wang, Philippos Mordohai
We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the o…
Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications
Tejas Mane, Aylar Bayramova, Kostas Daniilidis +2
We address the problem of estimating the shape of a person's head, defined as the geometry of the complete head surface, from a video taken with a single moving camera, and determi…
Do End-to-end Stereo Algorithms Under-utilize Information?
Changjiang Cai, Philippos Mordohai
Deep networks for stereo matching typically leverage 2D or 3D convolutional encoder-decoder architectures to aggregate cost and regularize the cost volume for accurate disparity es…
Matching-space Stereo Networks for Cross-domain Generalization
Changjiang Cai, Matteo Poggi, Stefano Mattoccia +1
End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occ…
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Matteo Poggi, Fabio Tosi, Konstantinos Batsos +2
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local,…
Oriented Point Sampling for Plane Detection in Unorganized Point Clouds
Bo Sun, Philippos Mordohai
Plane detection in 3D point clouds is a crucial pre-processing step for applications such as point cloud segmentation, semantic mapping and SLAM. In contrast to many recent plane d…
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai
Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due t…
Controlling a Robotic Stereo Camera Under Image Quantization Noise
Charles Freundlich, Yan Zhang, Alex Zihao Zhu +2
In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the cam…
3D Interest Point Detection via Discriminative Learning
Leizer Teran, Philippos Mordohai
The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while greatly describing human preference, can be ill-equippe…