RI: Large: Collaborative Research: Reconstructive recognition: Uniting statistical scene understanding and physics-based visual reasoning

该项目正在为计算机视觉创造一种新的范式，称为“重建识别”，它结合了以前基于机器学习的识别努力的最强元素和以前基于辐射推理的重建努力的最强元素。其目标是为机器感知提供一个新的基础，并为计算机视觉的应用提供变革性的进步的潜力。该项目寻求新的基于物理的识别方法，以及根据场景的物理解释像素值的基于学习的新方法。该议程围绕四个目标展开：Aim I开发了统一恢复形状、材料、运动和照明的通用重建过程。AIM II侧重于利用这种重建图像表征的有监督的视觉学习方法。AIM III追求无监督地发现与AIM I的工程模型相似的重建表示。最后，AIM IV引入了明确定义的挑战问题，这些问题聚焦于该领域，并作为对社会具有高度潜在影响的计算机视觉应用程序进展的可测量代理。这一项目将产生重大而广泛的影响，尤其是由于重新统一了目前对该领域存在分歧的认识和重建意见，从而改善了计算机视觉教学。更广泛地说，这个项目朝着机器可以看到的未来迈出了关键的一步，未来将给机器人、人机界面、安全和自动导航带来变化，仅举几例。

This project is creating a novel paradigm for computer vision, termed "reconstructive recognition", that incorporates the strongest elements of previous machine learning-based recognition efforts and the strongest elements of previous reconstruction efforts based on radiometric reasoning. The goal is to provide a new foundation for machine perception, and the potential for a transformative advance in applications of computer vision. The project seeks novel physics-based methods for recognition as well as novel learning-based methods for interpreting pixel values in terms of the physics of a scene. The agenda is structured around four aims: Aim I develops generalized reconstructive processes that unify the recovery of shape, materials, motion and illumination. Aim II focuses on supervised visual learning methods that exploit such reconstructive image representations. Aim III pursues unsupervised discovery of reconstructive representations that converge to be similar to the engineered models of Aim I. Finally, Aim IV introduces well-defined challenge problems that focus the field and serve as measurable proxies for progress in computer vision applications that have high potential impact on society. There is a significant broader impact to this project, not least being the improvement in computer vision pedagogy that ensues from a reunification of the currently divergent recognition and reconstruction views of the field. More broadly, this project pursues critical steps toward a future where machines can see, a future that will bring changes to robotics, human-computer interfaces, security, and autonomous navigation, to name a few.