  Thanks to the complementary nature of millimeter wave radar and camera, deep
learning-based radar-camera 3D object detection methods may reliably produce
accurate detections even in low-visibility conditions. This makes them
preferable to use in autonomous vehicles' perception systems, especially as the
combined cost of both sensors is cheaper than the cost of a lidar. Recent
radar-camera methods commonly perform feature-level fusion which often involves
projecting the radar points onto the same plane as the image features and
fusing the extracted features from both modalities. While performing fusion on
the image plane is generally simpler and faster, projecting radar points onto
the image plane flattens the depth dimension of the point cloud which might
lead to information loss and makes extracting the spatial features of the point
cloud harder. We proposed ClusterFusion, an architecture that leverages the
local spatial features of the radar point cloud by clustering the point cloud
and performing feature extraction directly on the point cloud clusters before
projecting the features onto the image plane. ClusterFusion achieved the
state-of-the-art performance among all radar-monocular camera methods on the
test slice of the nuScenes dataset with 48.7% nuScenes detection score (NDS).
We also investigated the performance of different radar feature extraction
strategies on point cloud clusters: a handcrafted strategy, a learning-based
strategy, and a combination of both, and found that the handcrafted strategy
yielded the best performance. The main goal of this work is to explore the use
of radar's local spatial and point-wise features by extracting them directly
from radar point cloud clusters for a radar-monocular camera 3D object
detection method that performs cross-modal feature fusion on the image plane.
