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Found 42 papers, 31 papers with code
MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos
Understanding human intentions and actions through egocentric videos is important on the path to embodied artificial intelligence.
RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning
To address this, we propose a novel paradigm, PR-by-PE localization, which improves global localization accuracy by deriving place recognition directly from pose estimation.
CV-MOS: A Cross-View Model for Motion Segmentation
When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects.
MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation
To effectively exploit complementary information, the motion branches of the proposed model combines motion features from both bird's eye view (BEV) and range view (RV) representations.
GOReloc: Graph-based Object-Level Relocalization for Visual SLAM
It determines the pose of a camera sensor by robustly associating the object detections in the current frame with 3D objects in a lightweight object-level map.
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
The major contributions of this study lie in comprehensively tightening the coupling between semantic segmentation and stereo matching.
SegNet4D: Effective and Efficient 4D LiDAR Semantic Segmentation in Autonomous Driving Environments
It entails identifying the semantic category of each point in the LiDAR scan and distinguishing whether it is dynamic, a critical aspect in downstream tasks such as path planning and autonomous navigation.
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation.
TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization
Our approach explicitly utilizes monocular depth priors through three key advancements: 1) we propose a novel depth-based ray sampling strategy based on the truncated normal distribution, which improves the convergence speed and accuracy of pose estimation; 2) to circumvent local minima and refine depth geometry, we introduce a coarse-to-fine training strategy that progressively improves the depth precision; 3) we propose a more robust inter-frame point constraint that enhances robustness against depth noise during training.
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics.
TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation
Visual place recognition (VPR) plays a pivotal role in autonomous exploration and navigation of mobile robots within complex outdoor environments.
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time.
Explicit Interaction for Fusion-Based Place Recognition
Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles.
VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks
Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process.
MF-MOS: A Motion-Focused Model for Moving Object Segmentation
Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field.
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Furthermore, the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios.
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach.
CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
Specifically, In the coarse matching phase, a novel I2P transformer module is employed to capture both homogeneous and heterogeneous global information from the image and point cloud data.
Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
In the end, we integrate our LCR-Net into a SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor driving environments.
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue.
Ranked #1 on
Semantic Segmentation
on SemanticPOSS
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences.
CCL: Continual Contrastive Learning for LiDAR Place Recognition
The experimental results show that our CCL consistently improves the performance of different methods in different environments outperforming the state-of-the-art continual learning method.
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
In this paper, we present a flexible and effective OIS framework for LiDAR point cloud that can accurately segment both known and unknown instances (i. e., seen and unseen instance categories during training).
InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation.
CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data
LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments.
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications.
IR-MCL: Implicit Representation-Based Online Global Localization
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion.
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
To address this limitation, we present a novel Bag of Words for real-time loop closing in 3D LiDAR SLAM, called BoW3D.
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects.
LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud
Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod
In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset).
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer
Given the fact that one same activity may be represented by videos in both high resolution (HR) and extreme low resolution (eLR), it is worth studying to utilize the relevant HR data to improve the eLR activity recognition.
