https://github.com/matthew29tang/pid-model
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
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.
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
Query-Efficient Imitation Learning for End-to-End Autonomous Driving
Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Socially Aware Motion Planning with Deep Reinforcement Learning
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.