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。

Point-DETR3D: 利用空间点先验与图像数据进行弱监督的 3D 物体检测

Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for  Weakly Semi-supervised 3D Object Detection

Key-point-based scene understanding is fundamental for autonomous driving
applications. At the same time, optical flow plays an important role in many
vision tasks. However, due to the implicit bias of equal attention on all
points, classic data-driven optical flow estimation methods yield less
satisfactory performance on key points, limiting their implementations in
key-point-critical safety-relevant scenarios. To address these issues, we
introduce a points-based modeling method that requires the model to learn
key-point-related priors explicitly. Based on the modeling method, we present
FocusFlow, a framework consisting of 1) a mix loss function combined with a
classic photometric loss function and our proposed Conditional Point Control
Loss (CPCL) function for diverse point-wise supervision; 2) a conditioned
controlling model which substitutes the conventional feature encoder by our
proposed Condition Control Encoder (CCE). CCE incorporates a Frame Feature
Encoder (FFE) that extracts features from frames, a Condition Feature Encoder
(CFE) that learns to control the feature extraction behavior of FFE from input
masks containing information of key points, and fusion modules that transfer
the controlling information between FFE and CFE. Our FocusFlow framework shows
outstanding performance with up to +44.5% precision improvement on various key
points such as ORB, SIFT, and even learning-based SiLK, along with exceptional
scalability for most existing data-driven optical flow methods like PWC-Net,
RAFT, and FlowFormer. Notably, FocusFlow yields competitive or superior
performances rivaling the original models on the whole frame. The source code
will be available at this https URL

基于关键点的场景理解对自动驾驶应用至关重要，同时光流在许多视觉任务中发挥着重要作用。本研究提出了一种基于点的建模方法，通过学习关键点先验信息来解决传统数据驱动光流估计方法在关键点上性能不佳的问题，并使用 FocusFlow 框架进行多样的点级监督，取得了显著的性能提升。