  Real-time efficient perception is critical for autonomous navigation and city
scale sensing. Orthogonal to architectural improvements, streaming perception
approaches have exploited adaptive sampling improving real-time detection
performance. In this work, we propose a learnable geometry-guided prior that
incorporates rough geometry of the 3D scene (a ground plane and a plane above)
to resample images for efficient object detection. This significantly improves
small and far-away object detection performance while also being more efficient
both in terms of latency and memory. For autonomous navigation, using the same
detector and scale, our approach improves detection rate by +4.1 $AP_{S}$ or
+39% and in real-time performance by +5.3 $sAP_{S}$ or +63% for small objects
over state-of-the-art (SOTA). For fixed traffic cameras, our approach detects
small objects at image scales other methods cannot. At the same scale, our
approach improves detection of small objects by 195% (+12.5 $AP_{S}$) over
naive-downsampling and 63% (+4.2 $AP_{S}$) over SOTA.
