PDF:3D Shape Reconstruction from Vision and Touch Project:3D-Vision-and-Touch Abstract If we want to build AI systems that can interact in and learn from the world around us, touch can be as equally important as sight and speech. If you’re asked about the shape of an object at hand, ...
PDF:Active 3D Shape Reconstruction from Vision and Touch Abstract Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory ...
Active 3D Shape Reconstruction from Vision and TouchEdward J. SmithDavid MegerLuis PinedaRoberto CalandraJitendra MalikAdriana RomeroMichal Drozdzal
Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based ...
* 题目: TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing* PDF: arxiv.org/abs/2311.1260* 作者: Mauro Comi,Yijiong Lin,Alex Church,Alessio Tonioni,Laurence Aitchison,Nathan F. Lepora* 其他: 10 pages, 8 figures* 相关: touchsdf.github.io/* 题目: PB...
* 题目: DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields* PDF: arxiv.org/abs/2303.1136* 作者: Wei-Ting Chen,Wang Yifan,Sy-Yen Kuo,Gordon Wetzstein* 其他: including supplemental material; project page: this https URL* 题目: Inversion by Direct ...
This paper addresses 3D object reconstruction from images acquired by camera-telecentric lense array. Firstly, we present a geometric model of an array camera-telecentric lens. Then we developed and implemented the calibration process using a planar chec
the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensembl...
预测的模型参数可以用来生成每个目标的二维图像或者深度图,与GroudTruth数据对比得到的Loss可以用来指导神经网络的学习。这个Loss称之为Render-and-Compare Loss,是基于OpenGL来实现的。3D-RCNN方法需要的输入数据比较多,Loss的设计也相对复杂,工程实现上难度较大。
{Reizenstein, Jeremy and Shapovalov, Roman and Henzler, Philipp and Sbordone, Luca and Labatut, Patrick and Novotny, David}, Booktitle = {International Conference on Computer Vision}, Title = {Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction}...