Considering that In areas that were not previously visible then the average Gaussian values are initialize around the meian rendering depth of the image with high variance that is with less confidence in their accuracy. If the inserte Gaussian is not visible in at least three previous frames then it is remove since it is unlikely that its presence is geometrically consiste Most of the incorrectly inserte Gaussians will be cleare during the optimization process because due to incorrect placement they will not satisfy the condition of joint visibility from different frames.
Mapping The last part
The method that we should consider is mapping. It helps maintain the consistency of the volumetric reconstruction and optimizes the parameters of the newly inserte Gaussians. We take keyframes from our window random frames from the past avoid forgetting Hong Kong Phone Number List the global structure of the scene. We obtain the set by which we will optimize the photometric error. To this error we add regularization which limits the strong stretching of Gaussians in space and we arrive at the following problem G forall k in where the same regularization for stretching the Gaussians.
The stretching coefficient
Gaussian overlinesis the average stretch over all Gaussians. results In general the quality and spee of the method are surprising Conclusion As practice shows machine learning methods cope quite well with solving the SLAM problem. This in turn blurs the line between the Taiwan Whatsapp Number List virtual world and the real one giving robots the opportunity like humans to receive and process visual information coming from the corresponding sensors. What does it have at the moment Teslas recently announce humanoid robots no longer use lidar radar or ultrasonic sensors.
