<transcript><audio_115>so the headset may be moving the camera maybe on the headset and moving you have to remove objects from the scene that you conclude are not moving just because the headset's moving now you have a the gyro and accelerometer on the headset so you may be able to figure out</transcript><caption><image_116>The image shows a man standing in front of a green chalkboard. He is wearing a blue t-shirt with a logo on it and appears to be giving a presentation. The man is pointing to something on the chalkboard with his right hand and has a serious expression on his face. On the blackboard behind him, there is text written in white chalk that reads "Natural features" and "Expect from natural scenes".</caption><transcript><audio_117>what motions are corresponding in the image plane you may be able to figure out what motions correspond to your own motion and which motions are not explained by that and then remove the ones that are not explained by that so it's not bad there's some information you can use there</transcript><caption><image_118>The image shows a man standing in front of a green chalkboard. He is wearing a blue t-shirt and appears to be writing on the chalkboard with white chalk. The text on the board reads "Natural features" and "Extract & maintain natural scenes". There is also a note that reads "Remove from scene" and a logo on the bottom right corner of the image. The man seems to be in a classroom or a classroom setting.</caption><transcript><audio_119>but generally it's difficult so i would say that overall using current technology the reliability is low you'll see good demonstrations but making this work very robustly in a</transcript><caption><image_120>The image shows a man standing in front of a green chalkboard. He is wearing a blue t-shirt with a logo on it and is holding a piece of paper in his hand. The man appears to be giving a presentation or explaining something. On the chalkboard, there is text written in white chalk that reads "Natural features - hard complex - Extract & maintain from - Remove moving object - Reliability low". There are also some yellow arrows pointing to different parts of the text.</caption><transcript><audio_121>very large number of settings and have sub millimeter level accuracy i haven't seen anything that can accomplish this yet so and so um so the so this is this is the kind of the state of the art this is a state of the art for natural features</transcript><transcript><audio_122>this then motivates people to go to</transcript><caption><image_123>The image shows a man standing in front of a blackboard in a classroom. He is wearing a blue t-shirt and is holding a whiteboard marker in his hand. On the blackboard, there are several bullet points written in white chalk. The bullet points are labeled "Features", "Natural features", "Hard core", "Extract & maintain from", "Remove moving objects", and "Reliability low". The man appears to be writing on the board in a serious manner.</caption> so the headset may be moving the camera maybe on the headset and moving you have to remove objects from the scene that you conclude are not moving just because the headset's moving now you have a the gyro and accelerometer on the headset so you may be able to figure out what motions are corresponding in the image plane you may be able to figure out what motions correspond to your own motion and which motions are not explained by that and then remove the ones that are not explained by that so it's not bad there's some information you can use there but generally it's difficult so i would say that overall using current technology the reliability is low you'll see good demonstrations but making this work very robustly in a very large number of settings and have sub millimeter level accuracy i haven't seen anything that can accomplish this yet so and so um so the so this is this is the kind of the state of the art this is a state of the art for natural features this then motivates people to go to
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