Search Results for author:
Found 51 papers, 26 papers with code
Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation
Our method benefits various applications including in-the-wild metrology monocular-SLAM, and 3D reconstruction, which highlight the versatility of Metric3D v2 models as geometric foundation models.
Ranked #1 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, e. g., depth and normals, from single images.
MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
To bridge this research gap, we propose MASSTAR: a Multi-modal lArge-scale Scene dataset with a verSatile Toolchain for surfAce pRediction and completion.
FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models
Our method achieves 40. 3 mean average precision (mAP) on the ScanNet semantic instance segmentation task.
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles.
G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model
Utilizing these GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR).
MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains.
Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap.
BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction
Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving.
Towards View-invariant and Accurate Loop Detection Based on Scene Graph
The current semantic-aided loop detection embeds the topology between semantic instances to search a loop.
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning
It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter.
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
In this paper, we propose a quality evaluation network to score the point clouds and help judge the quality of the point cloud before applying the completion model.
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
This paper proposes \textit{Contour Context}, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban utonomous driving scenario.
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications.
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well on the image while still keeping the geometric attribute on the 3D space, e. g., physical dimension, pairwise orthogonal, etc.
Temporal Point Cloud Completion with Pose Disturbance
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
Graph-Guided Deformation for Point Cloud Completion
In this paper, we propose a Graph-Guided Deformation Network, which respectively regards the input data and intermediate generation as controlling and supporting points, and models the optimization guided by a graph convolutional network(GCN) for the point cloud completion task.
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting
In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i. e., the motion segmentation problem) with a monocular event camera.
Trajectory Prediction with Graph-based Dual-scale Context Fusion
In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner.
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
We develop a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking
A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles.
An Augmented Reality Interaction Interface for Autonomous Drone
In this work, we build an AR interface that displays the reconstructed 3D map from the drone on physical surfaces in front of the operator.
Human-Computer Interaction Robotics
Event-based Stereo Visual Odometry
We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig.
RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight
In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight.
Robotics
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid.
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e. g., tracking noise and prediction errors, etc.).
Robotics
Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths
A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly.
Robotics
Flow-Motion and Depth Network for Monocular Stereo and Beyond
Third, beyond two-view depth estimation, we further extend the above networks to fuse depth information from multiple target images and estimate the depth map of the source image.
Multi-Sensor 3D Object Box Refinement for Autonomous Driving
We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving.
Real-time Scalable Dense Surfel Mapping
First, superpixels extracted from both intensity and depth images are used to model surfels in the system.
Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider.
An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors
Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm which finds a feasible trajectory with minimum control effort and time.
Learning Whole-Image Descriptors for Real-time Loop Detection andKidnap Recovery under Large Viewpoint Difference
Finally, we present the fully functional system with relative computation andhandling of multiple world co-ordinate system which is able to reduce odometry drift, recover fromcomplicated kidnap scenarios and random odometry failures.
Robotics
Real-Time Dense Stereo Embedded in A UAV for Road Inspection
This makes the damaged road areas more distinguishable from the road surface.
Probabilistic Dense Reconstruction from a Moving Camera
This paper presents a probabilistic approach for online dense reconstruction using a single monocular camera moving through the environment.
Robotics
Flying through a narrow gap using neural network: an end-to-end planning and control approach
To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning.
Robotics
FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots
We integrate FIESTA into a completed quadrotor system and validate it by both simulation and onboard experiments.
Robotics
Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents.
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern.
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
3D Object Detection From Stereo Images
Autonomous Driving
+3
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization.
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
We validate the performance of our system on public datasets and through real-world experiments with multiple sensors.
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion
By introducing an additional constraint in the time domain, our monocular visual-inertial tracking system can obtain continuous six degree of freedom (6-DoF) pose estimation without scale ambiguity.
Online Temporal Calibration for Monocular Visual-Inertial Systems
Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years.
MVDepthNet: Real-time Multiview Depth Estimation Neural Network
In this paper, we present MVDepthNet, a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios.
Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map.
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process.
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degrees-of-freedom (DOF) state estimation.
Robotics
