  A popular approach for constructing bird's-eye-view (BEV) representation in
3D detection is to lift 2D image features onto the viewing frustum space based
on explicitly predicted depth distribution. However, depth distribution can
only characterize the 3D geometry of visible object surfaces but fails to
capture their internal space and overall geometric structure, leading to sparse
and unsatisfactory 3D representations. To mitigate this issue, we present
BEV-IO, a new 3D detection paradigm to enhance BEV representation with instance
occupancy information. At the core of our method is the newly-designed instance
occupancy prediction (IOP) module, which aims to infer point-level occupancy
status for each instance in the frustum space. To ensure training efficiency
while maintaining representational flexibility, it is trained using the
combination of both explicit and implicit supervision. With the predicted
occupancy, we further design a geometry-aware feature propagation mechanism
(GFP), which performs self-attention based on occupancy distribution along each
ray in frustum and is able to enforce instance-level feature consistency. By
integrating the IOP module with GFP mechanism, our BEV-IO detector is able to
render highly informative 3D scene structures with more comprehensive BEV
representations. Experimental results demonstrate that BEV-IO can outperform
state-of-the-art methods while only adding a negligible increase in parameters
(0.2%) and computational overhead (0.24%in GFLOPs).
