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Opinion: We may have underestimated the role of the cameras themselves in robot datasets
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An author with experience in first-person robot data processing posted on Reddit r/humanoidrobotics, stating that storing only RGB frames makes observations difficult to localize in three-dimensional space, whereas camera intrinsics, distortion parameters, and stereo extrinsics, combined with matching algorithms, can recover depth in real units downstream. The author describes that their team, in an egocentric-view collection setup, synchronously records calibrated stereo video, camera poses, body skeleton, and hand coordinates, and combines this with motion tracking to support downstream tasks such as reconstruction, multi-sensor alignment, and cross-embodiment transfer. The author emphasizes that sensor and dataset design should be planned together, as calibration and synchronization errors can make the lowest-cost-to-collect datasets difficult to use.
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- Opinion: We may be underestimating the role of the camera itself in robot datasets
Drawing on experience with first-person robot data processing, the author points out that storing only RGB frames makes observations hard to localize in 3D space; camera intrinsics, distortion parameters, and stereo extrinsics, combined with matching algorithms, can recover depth in real units downstream. In its egocentric-view collection setup, the team synchronously records calibrated stereo video, camera poses, body skeleton, and hand coordinates, and combines this with motion tracking to support downstream tasks such as reconstruction, multi-sensor alignment, and cross-embodiment transfer. The author stresses that sensors and dataset design should be planned together, since calibration and synchronization errors can make the lowest-cost datasets hard to use.
Reddit r/humanoidrobotics ·