  Modern autonomous vehicles rely heavily on mechanical LiDARs for perception.
Current perception methods generally require 360{\deg} point clouds, collected
sequentially as the LiDAR scans the azimuth and acquires consecutive
wedge-shaped slices. The acquisition latency of a full scan (~ 100ms) may lead
to outdated perception which is detrimental to safe operation. Recent streaming
perception works proposed directly processing LiDAR slices and compensating for
the narrow field of view (FOV) of a slice by reusing features from preceding
slices. These works, however, are all based on a single modality and require
past information which may be outdated. Meanwhile, images from high-frequency
cameras can support streaming models as they provide a larger FoV compared to a
LiDAR slice. However, this difference in FoV complicates sensor fusion. To
address this research gap, we propose an innovative camera-LiDAR streaming 3D
object detection framework that uses camera images instead of past LiDAR slices
to provide an up-to-date, dense, and wide context for streaming perception. The
proposed method outperforms prior streaming models on the challenging NuScenes
benchmark. It also outperforms powerful full-scan detectors while being much
faster. Our method is shown to be robust to missing camera images, narrow LiDAR
slices, and small camera-LiDAR miscalibration.
