LiDAR-based 3D object detection is an indispensable task in advanced
autonomous driving systems. Though impressive detection results have been
achieved by superior 3D detectors, they suffer from significant performance
degeneration when facing unseen domains, such as different LiDAR
configurations, different cities, and weather conditions. The mainstream
approaches tend to solve these challenges by leveraging unsupervised domain
adaptation (UDA) techniques. However, these UDA solutions just yield
unsatisfactory 3D detection results when there is a severe domain shift, e.g.,
from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel
Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D),
where only a few labeled target data is available, yet can significantly
improve the adaptation performance. In particular, our SSDA3D includes an
Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the
first stage, an Inter-domain Point-CutMix module is presented to efficiently
align the point cloud distribution across domains. The Point-CutMix generates
mixed samples of an intermediate domain, thus encouraging to learn
domain-invariant knowledge. Then, in the second stage, we further enhance the
model for better generalization on the unlabeled target set. This is achieved
by exploring Intra-domain Point-MixUp in semi-supervised learning, which
essentially regularizes the pseudo label distribution. Experiments from Waymo
to nuScenes show that, with only 10% labeled target data, our SSDA3D can
surpass the fully-supervised oracle model with 100% target label. Our code is
available at this https URL.

提出了一种半监督领域自适应方法，名为 “SSDA3D”，包括两个阶段的 Point-CutMix 模块和 Intra-domain Generalization，很好地解决了不同 LiDAR 配置、城市和天气等领域间偏移问题，不依赖于大量标注数据，在只有 10％目标数据标注的情况下，可以超过 100％目标标签的完全监督模型。

SSDA3D: 3D 点云物体检测的半监督领域适应

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

Point-cloud based 3D object detectors recently have achieved remarkable
progress. However, most studies are limited to the development of network
architectures for improving only their accuracy without consideration of the
computational efficiency. In this paper, we first propose an autoencoder-style
framework comprising channel-wise compression and decompression via interchange
transfer-based knowledge distillation. To learn the map-view feature of a
teacher network, the features from teacher and student networks are
independently passed through the shared autoencoder; here, we use a compressed
representation loss that binds the channel-wised compression knowledge from
both student and teacher networks as a kind of regularization. The decompressed
features are transferred in opposite directions to reduce the gap in the
interchange reconstructions. Lastly, we present an head attention loss to match
the 3D object detection information drawn by the multi-head self-attention
mechanism. Through extensive experiments, we verify that our method can train
the lightweight model that is well-aligned with the 3D point cloud detection
task and we demonstrate its superiority using the well-known public datasets;
e.g., Waymo and nuScenes.

本文提出了一种基于自编码器的框架，通过基于交互转移的知识蒸馏实现通道级压缩和解压缩，同时结合多头自注意力机制进行 3D 物体检测信息匹配，从而训练出轻量级模型并在公共数据集中证明其卓越性能。

itKD：基于交替迁移的三维目标检测知识蒸馏

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object  Detection

Existing point-cloud based 3D object detectors use convolution-like operators
to process information in a local neighbourhood with fixed-weight kernels and
aggregate global context hierarchically. However, non-local neural networks and
self-attention for 2D vision have shown that explicitly modeling long-range
interactions can lead to more robust and competitive models. In this paper, we
propose two variants of self-attention for contextual modeling in 3D object
detection by augmenting convolutional features with self-attention features. We
first incorporate the pairwise self-attention mechanism into the current
state-of-the-art BEV, voxel and point-based detectors and show consistent
improvement over strong baseline models of up to 1.5 3D AP while simultaneously
reducing their parameter footprint and computational cost by 15-80% and 30-50%,
respectively, on the KITTI validation set. We next propose a self-attention
variant that samples a subset of the most representative features by learning
deformations over randomly sampled locations. This not only allows us to scale
explicit global contextual modeling to larger point-clouds, but also leads to
more discriminative and informative feature descriptors. Our method can be
flexibly applied to most state-of-the-art detectors with increased accuracy and
parameter and compute efficiency. We show our proposed method improves 3D
object detection performance on KITTI, nuScenes and Waymo Open datasets. Code
is available at this https URL

本研究提出了两种三维物体检测的自我注意力模型，即通过将卷积特征与自我注意力特征相结合，将成对的自我注意力机制纳入最新的基于 BEV、体素和点云检测器，最终在 KITTI 验证集上证明了模型精度的一致提高，同时减少了运算量和计算成本，实现了更好的参数效率和计算效率。