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Found 37 papers, 16 papers with code
DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation
It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinement module, and a temporal feature propagation module.
Holistically-Nested Structure-Aware Graph Neural Network for Road Extraction
Convolutional neural networks (CNN) have made significant advances in detecting roads from satellite images.
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields.
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding
To address this, we propose EMIE-MAP, a novel method for large-scale road surface reconstruction based on explicit mesh and implicit encoding.
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding
Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments.
Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving
Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision.
SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM
We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality rendering simultaneously.
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labelling
We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds.
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement
Furthermore we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow.
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation
Through the hash-based representation, we propose the Spherical Frustum sparse Convolution (SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points stored in spherical frustums respectively.
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Model
Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow.
SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views
By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results.
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation.
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation.
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization.
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally.
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion
The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds.
FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection
Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds.
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance
Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network.
Pseudo-LiDAR for Visual Odometry
In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted.
What Matters for 3D Scene Flow Network
Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.
Unsupervised Learning of 3D Scene Flow from Monocular Camera
Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions.
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking
Specifically, through multi-scale interactive query and fusion between pixel-level and point-level features, our method, can obtain more distinguishing features to improve the performance of multiple object tracking.
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation.
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video
In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer.
Efficient 3D Deep LiDAR Odometry
An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper.
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction
To solve the first problem, a novel context-aware set convolution layer is proposed in this paper to exploit contextual structure information of Euclidean space and learn soft aggregation weights for local point features.
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry
Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion.
Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos
Ablation studies demonstrate the effectiveness of the proposed inter-frame projection consistency constraints and intra-frame loop constraints.
Ranked #51 on
3D Human Pose Estimation
on Human3.6M
Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks
A new unsupervised learning method of depth and ego-motion using multiple masks from monocular video is proposed in this paper.
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points.
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper.
Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds
In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator.
Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers
Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end.
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds
In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer.
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of optical flow.
Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification
Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces.
