کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6937439 1449735 2018 30 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A scalable, efficient, and accurate solution to non-rigid structure from motion
ترجمه فارسی عنوان
یک راه حل مقیاس پذیر، کارآمد و دقیق برای ساختار غیر سفت و سخت از حرکت
کلمات کلیدی
فضای مسیر احتمالی، صحنه های مختلف زمان، ساختار غیر سفت و سخت از حرکت، نمایندگی نامناسب، فاکتور سازی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Most Non-Rigid Structure from Motion (NRSfM) solutions are based on factorization approaches that allow reconstructing objects parameterized by a sparse set of 3D points. These solutions, however, are low resolution and generally, they do not scale well to more than a few tens of points. While there have been recent attempts at bringing NRSfM to a dense domain, using for instance variational formulations, these are computationally demanding alternatives which require certain spatial continuity of the data, preventing their use for articulated shapes with large deformations or situations with multiple discontinuous objects. In this paper, we propose incorporating existing point trajectory low-rank models into a probabilistic framework for matrix normal distributions. With this formalism, we can then simultaneously learn shape and pose parameters using expectation maximization, and easily exploit additional priors such as known point correlations. While similar frameworks have been used before to model distributions over shapes, here we show that formulating the problem in terms of distributions over trajectories brings remarkable improvements, especially in generality and efficiency. We evaluate the proposed approach in a variety of scenarios including one or multiple objects, sparse or dense reconstructions, missing observations, mild or sharp deformations, and in all cases, with minimal prior knowledge and low computational cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 167, February 2018, Pages 121-133
نویسندگان
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