This is the Windows app named Theseus whose latest release can be downloaded as 0.2.2sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Theseus with OnWorks for free.
Sundin ang mga tagubiling ito upang patakbuhin ang app na ito:
- 1. Na-download ang application na ito sa iyong PC.
- 2. Ipasok sa aming file manager https://www.onworks.net/myfiles.php?username=XXXXX kasama ang username na gusto mo.
- 3. I-upload ang application na ito sa naturang filemanager.
- 4. Magsimula ng anumang OS OnWorks online emulator mula sa website na ito, ngunit mas mahusay na Windows online emulator.
- 5. Mula sa OnWorks Windows OS na kasisimula mo pa lang, pumunta sa aming file manager https://www.onworks.net/myfiles.php?username=XXXX gamit ang username na gusto mo.
- 6. I-download ang application at i-install ito.
- 7. I-download ang Wine mula sa iyong mga Linux distributions software repository. Kapag na-install na, maaari mong i-double click ang app upang patakbuhin ang mga ito gamit ang Wine. Maaari mo ring subukan ang PlayOnLinux, isang magarbong interface sa ibabaw ng Wine na tutulong sa iyong mag-install ng mga sikat na programa at laro sa Windows.
Ang alak ay isang paraan upang patakbuhin ang software ng Windows sa Linux, ngunit walang kinakailangang Windows. Ang alak ay isang open-source na layer ng compatibility ng Windows na maaaring direktang magpatakbo ng mga program sa Windows sa anumang desktop ng Linux. Sa totoo lang, sinusubukan ng Wine na muling ipatupad ang sapat na Windows mula sa simula upang mapatakbo nito ang lahat ng mga Windows application na iyon nang hindi talaga nangangailangan ng Windows.
MGA SCREENSHOT:
Theseus
DESCRIPTION:
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost weights, feature extractors, or initialization networks end-to-end. The implementation supports batched optimization on GPU, robust losses, damping strategies, and custom factors, making it practical for real-time systems. Helper packages provide geometry primitives and utilities for composing priors, relative constraints, and measurement models. Theseus bridges the gap between classical optimization and deep learning, enabling hybrid systems that learn components.
Mga tampok
- Differentiable Gauss-Newton and Levenberg–Marquardt solvers in PyTorch
- Factor-graph API with manifold variables like SE(3) and SO(3)
- Batched, GPU-accelerated solves with robust loss functions
- Autograd support to learn costs, features, or initializations end-to-end
- Geometry helpers and reusable factors for SLAM and bundle adjustment
- Extensible design for custom variables, factors, and damping policies
Wika ng Programming
Sawa
Kategorya
This is an application that can also be fetched from https://sourceforge.net/projects/theseus.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.