  Anchor-free object detectors are highly efficient in performing point-based
prediction without the need for extra post-processing of anchors. However,
different from the 2D grids, the 3D points used in these detectors are often
far from the ground truth center, making it challenging to accurately regress
the bounding boxes. To address this issue, we propose a Cascade Voting
(CascadeV) strategy that provides high-quality 3D object detection with
point-based prediction. Specifically, CascadeV performs cascade detection using
a novel Cascade Voting decoder that combines two new components: Instance Aware
Voting (IA-Voting) and a Cascade Point Assignment (CPA) module. The IA-Voting
module updates the object features of updated proposal points within the
bounding box using conditional inverse distance weighting. This approach
prevents features from being aggregated outside the instance and helps improve
the accuracy of object detection. Additionally, since model training can suffer
from a lack of proposal points with high centerness, we have developed the CPA
module to narrow down the positive assignment threshold with cascade stages.
This approach relaxes the dependence on proposal centerness in the early stages
while ensuring an ample quantity of positives with high centerness in the later
stages. Experiments show that FCAF3D with our CascadeV achieves
state-of-the-art 3D object detection results with 70.4\%<EMAIL_ADDRESS>and 51.6\%
<EMAIL_ADDRESS>on SUN RGB-D and competitive results on ScanNet. Code will be released
at https://github.com/Sharpiless/CascadeV-Det
