인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes a width-aligned transformer (WAFormer) based bird’s eye view (BEV) perception pipeline for 3D object detection using a multi-view camera. The proposed transformation method compresses multi-view image features along the vertical axis into 1D features, considerably reducing transformer computation. A refine transformer with self- and cross-attention restores vertically compressed information while preserving efficiency. To enhance spatial representation, we introduce a depth-altitude positional encoding that integrates depth distribution and ground-referenced altitude cues via height-aware aggregation and bidirectional cross-attention.
The overall network consists of a residual neural network (ResNet-101) backbone, the proposed view transformer, a BEV encoder, and a CenterPoint-style detection head, with an auxiliary fully convolutional, one-stage, object-detection based 1D (used only during training, and temporal alignment) using ego-motion for spatiotemporal feature construction. Under an identically trained baseline condition, the proposed method improves mAP, NDS, and inference speed compared with BEVDet4D. We also analyze the trade-off of vertical compression, that improves detection accuracy and velocity estimation but increases scale and orientation errors.
The overall network consists of a residual neural network (ResNet-101) backbone, the proposed view transformer, a BEV encoder, and a CenterPoint-style detection head, with an auxiliary fully convolutional, one-stage, object-detection based 1D (used only during training, and temporal alignment) using ego-motion for spatiotemporal feature construction. Under an identically trained baseline condition, the proposed method improves mAP, NDS, and inference speed compared with BEVDet4D. We also analyze the trade-off of vertical compression, that improves detection accuracy and velocity estimation but increases scale and orientation errors.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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