Search Results for author:
Found 20 papers, 5 papers with code
SC-Diff: 3D Shape Completion with Latent Diffusion Models
This paper introduces a 3D shape completion approach using a 3D latent diffusion model optimized for completing shapes, represented as Truncated Signed Distance Functions (TSDFs), from partial 3D scans.
CART: Caltech Aerial RGB-Thermal Dataset in the Wild
We present the first publicly available RGB-thermal dataset designed for aerial robotics operating in natural environments.
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning.
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
In this paper, we introduce a novel Multimodal, Multi-task CLIP adapting framework named \name to address these challenges, preserving both high supervised performance and robust transferability.
RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale
To circumvent this issue, we learn to augment versatile and robust monocular depth prediction with the dense metric scale induced from sparse and noisy Radar data.
FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding
This is achieved by distilling feature maps generated from image-based foundation models into those rendered from our 3D model.
NeRF-VO: Real-Time Sparse Visual Odometry with Neural Radiance Fields
We introduce a novel monocular visual odometry (VO) system, NeRF-VO, that integrates learning-based sparse visual odometry for low-latency camera tracking and a neural radiance scene representation for sophisticated dense reconstruction and novel view synthesis.
Dynamic LiDAR Re-simulation using Compositional Neural Fields
We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes.
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo
The sparse depth from VIO is firstly completed by a single-view depth completion network.
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications.
Correlation Pyramid Network for 3D Single Object Tracking
Additionally, considering the sparsity characteristics of the point clouds, we design a lateral correlation pyramid structure for the encoder to keep as many points as possible by integrating hierarchical correlated features.
High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF
Fine-detailed reconstructions are in high demand in many applications.
MBA-VO: Motion Blur Aware Visual Odometry
Motion blur is one of the major challenges remaining for visual odometry methods.
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings.
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust.
Robotics
Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation
Sensor calibration is the fundamental block for a multi-sensor fusion system.
Robotics
Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints
While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators.
LIC-Fusion: LiDAR-Inertial-Camera Odometry
This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points.
Robotics
Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization
In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors.
Robust Visual SLAM with Point and Line Features
In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features.
