Search Results for author:
Found 36 papers, 14 papers with code
Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning
The generalization capability has also been evaluated and a real-world application on the beach is also demonstrated.
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers
Excavators are crucial for diverse tasks such as construction and mining, while autonomous excavator systems enhance safety and efficiency, address labor shortages, and improve human working conditions.
HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes
The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings.
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models
In this paper, we propose RLingua, a framework that can leverage the internal knowledge of large language models (LLMs) to reduce the sample complexity of RL in robotic manipulations.
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand.
Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles
Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations.
Digging into Depth Priors for Outdoor Neural Radiance Fields
However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting.
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
The key insight is that map information can be utilized as a prior to guiding the training of the radiance fields with uncertainty.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
This paper presents a new method that uses scores provided by humans instead of pairwise preferences to improve the feedback efficiency of interactive reinforcement learning.
Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing
Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles.
NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering
This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately.
NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields
Using current NeRF training tools, a robot can train a NeRF environment model in real-time and, using our algorithm, identify 3D bounding boxes of objects of interest within the NeRF for downstream navigation or manipulation tasks.
VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain
We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces.
Semi-supervised 3D Object Detection with Proficient Teachers
To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed.
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination.
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC
With the nonlinear dynamic model of a wheel loader, nonlinear model predictive control (MPC) is used in offline trajectory planning to obtain a high-performance state-control trajectory while satisfying the state and control constraints.
Construction Site Safety Monitoring and Excavator Activity Analysis System
Our perception system could detect multi-class construction machines and humans in real-time while estimating the poses and actions of the excavator.
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed by fitting the deformed 3D object model and the object mask in the 2D image.
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected.
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module.
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection
Finally, we verify the proposed framework on the public KITTI dataset with different 3D object detectors.
Text2Video: Text-driven Talking-head Video Synthesis with Personalized Phoneme-Pose Dictionary
With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic.
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity.
Robust 2D/3D Vehicle Parsing in CVIS
We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS.
MapFusion: A General Framework for 3D Object Detection with HDMaps
In this paper, we propose a simple but effective framework - MapFusion to integrate the map information into modern 3D object detector pipelines.
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed.
Ranked #20 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS
We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS.
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input.
Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation
For autonomous driving, the dynamics and states of vehicle parts such as doors, the trunk, and the bonnet can provide meaningful semantic information and interaction states, which are essential to ensuring the safety of the self-driving vehicle.
Object-aware Feature Aggregation for Video Object Detection
As a consequence, augmenting features with such prior knowledge can effectively improve the classification and localization performance.
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
