  Scene Graph Generation is a critical enabler of environmental comprehension
for autonomous robotic systems. Most of existing methods, however, are often
thwarted by the intricate dynamics of background complexity, which limits their
ability to fully decode the inherent topological information of the
environment. Additionally, the wealth of contextual information encapsulated
within depth cues is often left untapped, rendering existing approaches less
effective. To address these shortcomings, we present STDG, an avant-garde
Depth-Guided One-Stage Scene Graph Generation methodology. The innovative
architecture of STDG is a triad of custom-built modules: The Depth Guided HHA
Representation Generation Module, the Depth Guided Semi-Teaching Network
Learning Module, and the Depth Guided Scene Graph Generation Module. This
trifecta of modules synergistically harnesses depth information, covering all
aspects from depth signal generation and depth feature utilization, to the
final scene graph prediction. Importantly, this is achieved without imposing
additional computational burden during the inference phase. Experimental
results confirm that our method significantly enhances the performance of
one-stage scene graph generation baselines.
