Non-Rigid Puzzles

Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames

This paper proposes a new approach for monocular dense 3D reconstruction of a complex dynamic scene from two perspective frames. By applying superpixel over-segmentation to the image, we model a generically dynamic (hence non-rigid) scene with a piecewise planar and rigid approximation. In this way, we reduce the dynamic reconstruction problem to a "3D jigsaw puzzle" problem which takes pieces from an unorganized "soup of superpixels". We show that our method provides an effective solution to the inherent relative scale ambiguity in structure-from-motion. Since our method does not assume a tem…

具有任意交叉切割的图形和非图形多边形拼图

Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts

本文提出了一种新的拼图问题，通过任意数量的直线切割生成一般凸多边形拼图，并通过多体弹簧质量动力系统和分层重建过程解决其中的挑战，实验表明可自动解决图形和非图形拼图。

SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes

We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene. SplitFusion first adopts deep learning based semantic instant segmentation technique to split the scene into rigid or non-rigid surfaces. The split surfaces are independently tracked via rigid or non-rigid ICP and reconstructed through incremental depth map fusion. Experimental results show that the proposed approach can provide not only accurate environment maps but also well-reconstructed non-rigid targets, e.g. the mo…

Eye-Q：一个多语言视觉词谜解决和图像到短语推理的基准

Eye-Q: A Multilingual Benchmark for Visual Word Puzzle Solving and Image-to-Phrase Reasoning

Eye-Q提出一个多语言视觉词谜基准，评估模型在复杂视觉推理中的能力，发现现有模型在抽象和跨语言推理上存在显著差距。

A Comparative Study for Non-rigid Image Registration and Rigid Image Registration

Image registration algorithms can be generally categorized into two groups: non-rigid and rigid. Recently, many deep learning-based algorithms employ a neural net to characterize non-rigid image registration function. However, do they always perform better? In this study, we compare the state-of-art deep learning-based non-rigid registration approach with rigid registration approach. The data is generated from Kaggle Dog vs Cat Competition \url{https://www.kaggle.com/c/dogs-vs-cats/} and we test the algorithms' performance on rigid transformation including translation, rotation, scaling, shear…