  Camera-only 3D detection provides an economical solution with a simple
configuration for localizing objects in 3D space compared to LiDAR-based
detection systems. However, a major challenge lies in precise depth estimation
due to the lack of direct 3D measurements in the input. Many previous methods
attempt to improve depth estimation through network designs, e.g., deformable
layers and larger receptive fields. This work proposes an orthogonal direction,
improving the camera-only 3D detection by introducing multi-agent
collaborations. Our proposed collaborative camera-only 3D detection (CoCa3D)
enables agents to share complementary information with each other through
communication. Meanwhile, we optimize communication efficiency by selecting the
most informative cues. The shared messages from multiple viewpoints
disambiguate the single-agent estimated depth and complement the occluded and
long-range regions in the single-agent view. We evaluate CoCa3D in one
real-world dataset and two new simulation datasets. Results show that CoCa3D
improves previous SOTA performances by 44.21% on DAIR-V2X, 30.60% on OPV2V+,
12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a
potential that with sufficient collaboration, the camera might overtake LiDAR
in some practical scenarios. We released the dataset and code at
https://siheng-chen.github.io/dataset/CoPerception+ and
https://github.com/MediaBrain-SJTU/CoCa3D.
