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This repository will provide tools and news about The OmniScape Dataset

Home Page: https://git.io/omniscape

dataset omnidirectional spherical fisheye catadioptric semantic-segmentation depth-estimation depth-map gta5 gtav

omniscape's Issues

Adjusting the camera angle and data recording/generation for Omniscape

To the Omniscape team:-

I have a few questions relating to the camera position and the way the images were recorded for the dataset. How did you determine the camera position for simulation on the ego vehicle for different views. Also how was the recording of the images performed? Is there a process for determining that 10,000 images will be recorded from CARLA at once and stored in a single directory for your dataset (was this done with a bash script or all in Python)? Also was there a process by which the different camera views were separated from each other in the dataset or were they all placed into one directory?

Thank you for your time and I congratulate you for making a successful publication for your research! Will any code for your dataset generation be released on github?

How can I simulate a fish eye camera in carla?

Hello, I want to know how to configure a fish eye camera in carla.
I just found "Camera lens distortion attributes" in sensors reference of carla, but I can't get the effect I want.
Camera lens distortion attributes
my fish eye camera
normal camera with 90fov
I looking forward to your answer, thank you very much.

Choice of spherical projection

Out of curiosity, what's the purpose of using an orthographic projection to distribute the dataset? You end up throwing away a lot of information in the boundaries due to the distortion and pixel discretization. The distortion is going to exist regardless, but it seems to me that a format with expansive distortion (i.e. 1 undistorted pixel is spread into many pixels) is better than a format with contractive distortion (i.e. many undistorted pixels get condensed into a single pixel). Something like a cropped equirectangular projection (with associated metadata detailing the FOV) would serve this purpose better. It's not difficult to resample these orthographic projections to equirectangular projections, but you've already lost the data due to discretization in the original orthographic projections. Because it's all virtual data anyway, it seems there shouldn't be any limitations (other than time) to rendering the dataset to a less lossy format.

Does the database contain 6D pose (rotation and translation)?

Hi,

Can you please clarify if your database will contain 6D pose information with both rotation and translation for every frame?
From your paper, I saw that you've orientation ground truth, which I guess will give rotation values. Will the ground truth also contain translation values?

Basically, I'm try to warp a frame to the view of next frame. For this, I'll need depth, pose of both frames and camera intrinsic matrix. Will all these data be available, at least for one of left or right camera?

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