6G networks are expected to sense their surroundings using the same waveforms that carry data. We ask how much can be learned about the shape of an object from the terahertz echoes that user equipment transmissions produce as they scatter from that object on their way to a ceiling-mounted access point, with no sensing hardware beyond the communication link itself. We build a physics-based multistatic forward model at a 300 GHz carrier with 50 GHz of bandwidth, combining Physical Optics (PO) and Physical Theory of Diffraction (PTD) scattering with an image-source multipath lattice, molecular absorption and directional horn antennas. The model makes explicit a property of the PO mechanism hierarchy that is usually left implicit. Six physically distinct mechanisms share only three frequency exponents, so the spectral slope does not by itself determine which mechanisms a target presents and therefore cannot by itself determine its geometry. Angular diversity is accordingly a requirement for identifiability rather than an optional refinement. We compare a transmitter-count-invariant 2D-CNN with a physics-informed RBF-SVM on object orientations never seen in training. Accuracy climbs from roughly 70 percent with one transmitter to 96-97 percent with 20. The CNN gains in two regimes, irregular placement and lossy targets, while on metal under quasi-uniform placement the SVM baseline is fully competitive. Performance falls off gradually under occlusion, holding 78 percent with three quarters of the surface hidden and 63 percent when the classifier has never seen an occluded target. Multi-object scenes are limited by interference between overlapping returns rather than by angular coverage, so presence detection saturates after roughly four transmitters.
