1. A computer-implemented method for defining a model comprising: at a computer system having one or more processors and memory storing one or more programs that when executed by the one or more processors cause the computer system to perform the method: analyzing one or more images of a physical space that include a plurality of distinctive visual features, wherein: the plurality of distinctive visual features include a first marker associated with first semantic information and a second marker associated with second semantic information; the first semantic information and the second semantic information are defined in accordance with a markup language that specifies rules for combining semantic information from a plurality of markers; and analyzing the one or more images includes: determining a pose of the first marker; and determining a pose of the second marker; and defining a model based at least in part on the pose of the first marker, the pose of the second marker, the first semantic information and the second semantic information, wherein defining the model includes: approximating a first model component based on the pose of the first marker and the first semantic information; and modifying the first model component based on the pose of the second marker and the second semantic information.
1. A computer-implemented method for defining a model comprising: at a computer system having one or more processors and memory storing one or more programs that when executed by the one or more processors cause the computer system to perform the method: analyzing one or more images of a physical space that include a plurality of distinctive visual features, wherein: the plurality of distinctive visual features include a first marker associated with first semantic information and a second marker associated with second semantic information; the first semantic information and the second semantic information are defined in accordance with a markup language that specifies rules for combining semantic information from a plurality of markers; and analyzing the one or more images includes: determining a pose of the first marker; and determining a pose of the second marker; and defining a model based at least in part on the pose of the first marker, the pose of the second marker, the first semantic information and the second semantic information, wherein defining the model includes: approximating a first model component based on the pose of the first marker and the first semantic information; and modifying the first model component based on the pose of the second marker and the second semantic information. 4. The method of claim 1 , wherein: the model includes a plurality of model aspects; and at least one of the model aspects is specified in accordance with semantic information associated with a subset of the markers, the subset including at least two markers.