MambaDETR: Using a State Space Model for Efficient Temporal Modeling in Multi-View 3D Object Detection
MambaDETR is a novel method for multi-view 3D object detection that leverages a state space model for efficient temporal fusion, outperforming traditional transformer-based approaches in long-range temporal modeling while maintaining linear computational complexity.
고정 프레임 집계의 성능 저하 문제를 해결하는 LiDAR 기반 3D 객체 감지 모델: VADet (VADet: 가변 집계를 사용한 다중 프레임 LiDAR 3D 객체 감지)
LiDAR 기반 3D 객체 감지에서 고정 프레임 집계 방식은 객체의 움직임에 따라 성능이 저하되는 문제가 발생하는데, VADet은 객체별로 최적의 프레임 수를 가변적으로 집계하여 이러한 문제를 해결하고 더 높은 성능을 달성한다.
RayFormer: Enhancing Multi-Camera 3D Object Detection by Aligning Query Initialization and Feature Sampling with Camera Optics
RayFormer improves the accuracy of query-based multi-camera 3D object detection by aligning the initialization and feature extraction of object queries with the optical characteristics of cameras, mitigating ambiguity in query features and enhancing detection accuracy.
効率的なビュー変換によるマルチモーダル3Dオブジェクト検出：EVT
EVTは、LiDARとカメラのデータを融合して3Dオブジェクト検出を行う、効率的かつ高精度な新しい手法である。
EVT: An Efficient 3D Object Detection Method Using LiDAR-Camera Fusion and Enhanced Transformer Design
EVT, a novel 3D object detection method, leverages LiDAR-camera fusion through an efficient view transformation process and an enhanced transformer architecture to achieve state-of-the-art performance in accuracy and speed.
3D 객체 탐지 성능 향상을 위한 협업 개선 기술: Co-Fix3D
Co-Fix3D는 LiDAR 및 LiDAR-카메라 융합 데이터에서 3D 객체 탐지 성능을 향상시키기 위해 BEV(Bird's Eye View) 기능을 개선하는 새로운 딥러닝 프레임워크입니다.
Co-Fix3D：協調的なリファインメントによる3Dオブジェクト検出の強化 - 鳥瞰図特徴の改善に焦点を当てる
Co-Fix3Dは、LiDARベースおよびLiDAR-カメラ融合の3Dオブジェクト検出において、鳥瞰図（BEV）特徴を改善することで、特に複雑な環境下での精度を向上させる。
SC3D: A Novel Approach to Label-Efficient 3D Object Detection Using Single Click Annotation for Autonomous Driving
This research paper introduces SC3D, a novel method for 3D object detection that significantly reduces annotation effort by using single-click annotations on point cloud data, achieving comparable performance to fully supervised methods while requiring only 0.2% of the labeling cost.
Co-Fix3D: A Novel Approach to 3D Object Detection Using Collaborative Refinement for Enhanced BEV Feature Optimization
Co-Fix3D is a new 3D object detection framework that enhances the accuracy of autonomous driving systems by refining Bird's Eye View (BEV) features through a multi-stage Local and Global Enhancement (LGE) module, leading to improved identification and localization of objects, especially in challenging scenarios.
악천후 속 강력한 3D 객체 감지를 위한 LiDAR-4D 레이더 융합: L4DR
LiDAR-4D 레이더 센서 융합 기술을 사용하여 악천후 조건에서도 강력한 성능을 보이는 3D 객체 감지 방법론을 제시합니다.
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