  To promote better performance-bandwidth trade-off for multi-agent perception,
we propose a novel distilled collaboration graph (DiscoGraph) to model
trainable, pose-aware, and adaptive collaboration among agents. Our key
novelties lie in two aspects. First, we propose a teacher-student framework to
train DiscoGraph via knowledge distillation. The teacher model employs an early
collaboration with holistic-view inputs; the student model is based on
intermediate collaboration with single-view inputs. Our framework trains
DiscoGraph by constraining post-collaboration feature maps in the student model
to match the correspondences in the teacher model. Second, we propose a
matrix-valued edge weight in DiscoGraph. In such a matrix, each element
reflects the inter-agent attention at a specific spatial region, allowing an
agent to adaptively highlight the informative regions. During inference, we
only need to use the student model named as the distilled collaboration network
(DiscoNet). Attributed to the teacher-student framework, multiple agents with
the shared DiscoNet could collaboratively approach the performance of a
hypothetical teacher model with a holistic view. Our approach is validated on
V2X-Sim 1.0, a large-scale multi-agent perception dataset that we synthesized
using CARLA and SUMO co-simulation. Our quantitative and qualitative
experiments in multi-agent 3D object detection show that DiscoNet could not
only achieve a better performance-bandwidth trade-off than the state-of-the-art
collaborative perception methods, but also bring more straightforward design
rationale. Our code is available on https://github.com/ai4ce/DiscoNet.
