Training high-accuracy 3D detectors necessitates massive labeled 3D
annotations with 7 degree-of-freedom, which is laborious and time-consuming.
Therefore, the form of point annotations is proposed to offer significant
prospects for practical applications in 3D detection, which is not only more
accessible and less expensive but also provides strong spatial information for
object localization.In this paper, we empirically discover that it is
non-trivial to merely adapt Point-DETR to its 3D form, encountering two main
bottlenecks: 1) it fails to encode strong 3D prior into the model, and 2) it
generates low-quality pseudo labels in distant regions due to the extreme
sparsity of LiDAR points. To overcome these challenges, we introduce
Point-DETR3D, a teacher-student framework for weakly semi-supervised 3D
detection, designed to fully capitalize on point-wise supervision within a
constrained instance-wise annotation budget.Different from Point-DETR which
encodes 3D positional information solely through a point encoder, we propose an
explicit positional query initialization strategy to enhance the positional
prior. Considering the low quality of pseudo labels at distant regions produced
by the teacher model, we enhance the detector's perception by incorporating
dense imagery data through a novel Cross-Modal Deformable RoI Fusion
(D-RoI).Moreover, an innovative point-guided self-supervised learning technique
is proposed to allow for fully exploiting point priors, even in student
models.Extensive experiments on representative nuScenes dataset demonstrate our
Point-DETR3D obtains significant improvements compared to previous works.
Notably, with only 5% of labeled data, Point-DETR3D achieves over 90%
performance of its fully supervised counterpart.

基于点注释的半监督 3D 检测方法 Point-DETR3D 通过引入显式的位置查询初始化策略、使用交叉模态可变形 RoI 融合和点引导的自监督学习技术，充分利用点先验，并在仅有 5% 标注数据的情况下显著改进 3D 检测性能。