Core Concepts
CN-RMA introduces an innovative approach for 3D indoor object detection from multi-view images, achieving state-of-the-art performance by combining reconstruction and detection networks with occlusion-aware feature aggregation.
Abstract
CN-RMA addresses the challenge of ambiguity in image and 3D correspondence without explicit geometry.
The method leverages 3D reconstruction networks and object detection networks for accurate feature voting in 3D space.
By incorporating Ray Marching Aggregation (RMA), CN-RMA effectively detects occlusions and achieves superior performance in 3D object detection.
The approach is evaluated on ScanNet and ARKitScenes datasets, outperforming existing methods in mAP@0.25 and mAP@0.5.
CN-RMA's training scheme involves pre-training the MVS module and detection network, followed by joint fine-tuning for optimal performance.
Stats
CN-RMA는 3D 실내 물체 감지를 위한 혁신적인 방법을 소개합니다.
방법은 3D 재구성 네트워크와 물체 감지 네트워크를 결합하여 3D 공간에서 정확한 특징 투표를 달성합니다.
CN-RMA는 Ray Marching Aggregation (RMA)을 통합하여 효과적으로 가려짐을 감지하고 3D 물체 감지에서 우수한 성능을 달성합니다.
Quotes
"CN-RMA leverages the synergy of 3D reconstruction networks and 3D object detection networks for accurate feature voting in 3D space."
"Our method achieves state-of-the-art performance in 3D object detection from multi-view images."