Search Results for author:
Found 27 papers, 10 papers with code
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
Through STV grids, points within each pillar are individually encoded using Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE).
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection.
PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network
The PillarGen model performs the following three steps: 1) pillar encoding, 2) Occupied Pillar Prediction (OPP), and 3) Pillar to Point Generation (PPG).
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition
Various types of sensors have been considered to develop human action recognition (HAR) models.
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection.
Ranked #4 on
3D Object Detection
on nuscenes Camera-Radar
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements.
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation.
Ranked #2 on
3D Multi-Object Tracking
on nuscenes Camera-Radar
MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection
While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets.
Ranked #25 on
3D Object Detection
on nuScenes
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection.
Ranked #1 on
3D Object Detection
on KITTI Cars Hard
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
The trajectory refinement network enhances each of the M proposals using 1) tube-query scene attention (TQSA) and 2) proposal-level interaction attention (PIA) mechanisms.
Ranked #13 on
Motion Forecasting
on Argoverse CVPR 2020
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision
In this way, our 3D detection network can be supervised by more depth supervision from raw LiDAR points, which does not require any human annotation cost, to estimate accurate depth without explicitly predicting the depth map.
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence
LiDAR sensors are widely used for 3D object detection in various mobile robotics applications.
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR.
Ranked #6 on
3D Object Detection
on nuscenes Camera-Radar
Learning from Data with Noisy Labels Using Temporal Self-Ensemble
By combining the aforementioned metrics, we present the proposed {\it self-ensemble-based robust training} (SRT) method, which can filter the samples with noisy labels to reduce their influence on training.
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning
The student models are given a fixed number of temporal self-ensemble models, and the teacher model is constructed by averaging the weights of the student models.
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition
There is growing interest in the challenging visual perception task of learning from long-tailed class distributions.
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
The detector constructs the spatio-temporal features via the weighted temporal aggregation of the spatial features obtained by the camera and LiDAR fusion.
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents
In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment.
Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave)communications.
Joint Representation of Temporal Image Sequences and Object Motion for Video Object Detection
In this paper, we propose a new video object detector (VoD) method referred to as temporal feature aggregation and motion-aware VoD (TM-VoD), which produces a joint representation of temporal image sequences and object motion.
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
First, the method employs auto-calibrated projection, to transform the 2D camera features to a smooth spatial feature map with the highest correspondence to the LiDAR features in the bird's eye view (BEV) domain.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection
Convolutional neural network (CNN) has led to significant progress in object detection.
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance.
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder.
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model.
Autonomous Braking System via Deep Reinforcement Learning
In this paper, we propose a new autonomous braking system based on deep reinforcement learning.
