arXiv:2312.17010
Robust Multi-Modal Image Stitching for Improved Scene Understanding
원문 초록
Multi-modal image stitching can be a difficult feat. That's why, in this paper, we've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality - irrespective of lighting, scale or orientation differences between images. We've put our pipeline to the test with a varied dataset and found that it's very effective in enhancing scene understanding and finding real-world applications.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.