4 ms·

This has been possible for a good while, the most common algorithm is Parallel Tracking and Mapping (PTAM). No stereo cameras are needed, but there are also imp

This has been possible for a good while, the most common algorithm is Parallel Tracking and Mapping (PTAM). No stereo cameras are needed, but there are also implementations that make use of depth sensors. What interests me is for an open implementation of Street View: drones plus crowdsourcing and we'd be free of requiring Google services for street visualization. We already have open street maps, we could have open street view too.

gauku 7y ago
For a course project, I implemented DTAM - which does dense mapping and tracking, without needing any stereo vision, in real time (on a mid-range GPU). It's really amazing.
- stelonix 7y ago
  Hadn't heard of DTAM yet, but found a video and am very impressed! What GPU did you use? Do you think it'd be feasible on mobile GPUs?
  - saganus 7y ago
    That sounds interesting. Any links to further explain what DTAM is/how it works?
    - severine 7y ago
      DTAM: Dense tracking and mapping in real-time > DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application. https://ieeexplore.ieee.org/document/6126513 https://ieeexplore.ieee.org/document/6126513
      - saganus 7y ago
        Awesome, thanks!
    - gauku 7y ago
      I found this post summarizes the concepts step by step and is really helpful. http://ahumaninmachinesworld.blogspot.com/2015/07/dtam-dense-tracking-and-mapping.html http://ahumaninmachinesworld.blogspot.com/2015/07/dtam-dense...
  - gauku 7y ago
    Unfortunately, I don't know CUDA yet and didn't implement a GPU accelerated version. I can't comment on its feasibility on mobile GPUs but the paper authors reported real-time performance on a GTX-480 + i7 quad-core CPU system.

gauku 7y ago

For a course project, I implemented DTAM - which does dense mapping and tracking, without needing any stereo vision, in real time (on a mid-range GPU). It's really amazing.

stelonix 7y ago
Hadn't heard of DTAM yet, but found a video and am very impressed! What GPU did you use? Do you think it'd be feasible on mobile GPUs?
- saganus 7y ago
  That sounds interesting. Any links to further explain what DTAM is/how it works?
  - severine 7y ago
    DTAM: Dense tracking and mapping in real-time > DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application. https://ieeexplore.ieee.org/document/6126513 https://ieeexplore.ieee.org/document/6126513
    - saganus 7y ago
      Awesome, thanks!
  - gauku 7y ago
    I found this post summarizes the concepts step by step and is really helpful. http://ahumaninmachinesworld.blogspot.com/2015/07/dtam-dense-tracking-and-mapping.html http://ahumaninmachinesworld.blogspot.com/2015/07/dtam-dense...
- gauku 7y ago
  Unfortunately, I don't know CUDA yet and didn't implement a GPU accelerated version. I can't comment on its feasibility on mobile GPUs but the paper authors reported real-time performance on a GTX-480 + i7 quad-core CPU system.