Search Results for author:
Found 19 papers, 3 papers with code
OpenSU3D: Open World 3D Scene Understanding using Foundation Models
In this paper, we present a novel, scalable approach for constructing open set, instance-level 3D scene representations, advancing open world understanding of 3D environments.
LightGBM robust optimization algorithm based on topological data analysis
To enhance the robustness of the Light Gradient Boosting Machine (LightGBM) algorithm for image classification, a topological data analysis (TDA)-based robustness optimization algorithm for LightGBM, TDA-LightGBM, is proposed to address the interference of noise on image classification.
DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving.
ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation
Data generation is recognized as a potent strategy for unsupervised domain adaptation (UDA) pertaining semantic segmentation in adverse weathers.
Implicit Learning of Scene Geometry from Poses for Global Localization
In this paper, we propose to utilize these minimal available labels (. i. e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose.
Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras
In this work, we address the problem of estimating the 6 DoF camera pose relative to a global frame from a single image.
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap.
DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation
Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage.
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation
In this paper, we propose LoopDA for domain adaptive nighttime semantic segmentation.
Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects
Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks.
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.
A Real World Dataset for Multi-view 3D Reconstruction
We present a dataset of 998 3D models of everyday tabletop objects along with their 847, 000 real world RGB and depth images.
Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of Indoor Environments
In this paper, we propose a generalizable method that systematically combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction.
Online Semantic Exploration of Indoor Maps
In this paper we propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning.
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy
In this paper, a motion capturing algorithm is proposed for upper body motion tracking.
Extracting Semantic Indoor Maps from Occupancy Grids
The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world.
A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on Markov Logic Networks and Data Driven MCMC
In this paper, we propose a generalizable knowledge framework for data abstraction, i. e. finding compact abstract model for input data using predefined abstract terms.
Table-Top Scene Analysis Using Knowledge-Supervised MCMC
Our approach to generate scene graphs is probabilistic: Uncertainty in the object poses is addressed by a probabilistic sensor model that is embedded in a data driven MCMC process.
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.
