https://github.com/JLrumberger/SpatialEmbeddingLoss
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
Learning Trajectory Dependencies for Human Motion Prediction
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks
Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.