https://github.com/MindSpore-paper-code-2/code2/tree/main/OctSqueeze
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
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.
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
PointPillars: Fast Encoders for Object Detection from Point Clouds
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
Mask-Guided Attention Network for Occluded Pedestrian Detection
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
Mask-Guided Attention Network for Occluded Pedestrian Detection
Uncertainty Calibration and its Application to Object Detection
Stereo R-CNN based 3D Object Detection for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.