멀티 카메라/뷰 퓨전 관련 글 목록

• 참조 : https://towardsdatascience.com/monocular-birds-eye-view-semantic-segmentation-for-autonomous-driving-ee2f771afb59
• 참조 : https://towardsdatascience.com/monocular-bev-perception-with-transformers-in-autonomous-driving-c41e4a893944
• 참조 : https://towardsdatascience.com/monocular-3d-lane-line-detection-in-autonomous-driving-4d7cdfabf3b6

• 멀티 카메라/뷰 퓨전을 이용한 다양한 어플리케이션 관련 글 목록 입니다.

멀티 카메라 기반 BEV(Bird Eye View) Sementic Segmentation

• Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks
• Learning to Look around Objects for Top-View Representations of Outdoor Scenes
• A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View
• MonoLayout (Amodal scene layout from a single image)
• PyrOccNet, Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks)
• Lift, Splat, Shoot (Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D)
• Projecting Your View Attentively (Monocular Road Scene Layout Estimation via Cross-view Transformation)
• Bird’s-Eye-View Panoptic Segmentation Using Monocular Frontal View Images
• BEV-Seg (Bird’s Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud)

멀티 카메라 기반 Feature Fusion 연구

• Vision-Centric BEV Perception (Survey)
• Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion
• NVAutoNet, FAST AND ACCURATE 360◦ 3D VISUAL PERCEPTION FOR SELF DRIVING
• Translating Images Into Maps
• Simple BEV

멀티 뷰 기반의 3D reconstruction

• NeRF, Representing Scenes as Neural Radiance Fields for View Synthesis