Affordable 3D scanners often produce sparse and non-uniform point clouds that
negatively impact downstream applications in robotic systems. While existing
point cloud upsampling architectures have demonstrated promising results on
standard benchmarks, they tend to experience significant performance drops when
the test data have different distributions from the training data. To address
this issue, this paper proposes a test-time adaption approach to enhance model
generality of point cloud upsampling. The proposed approach leverages
meta-learning to explicitly learn network parameters for test-time adaption.
Our method does not require any prior information about the test data. During
meta-training, the model parameters are learned from a collection of
instance-level tasks, each of which consists of a sparse-dense pair of point
clouds from the training data. During meta-testing, the trained model is
fine-tuned with a few gradient updates to produce a unique set of network
parameters for each test instance. The updated model is then used for the final
prediction. Our framework is generic and can be applied in a plug-and-play
manner with existing backbone networks in point cloud upsampling. Extensive
experiments demonstrate that our approach improves the performance of
state-of-the-art models.

测试时间适应性元学习方法在点云上采样中提高模型性能。

利用元学习进行点云上采样的测试时间调整

Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning

Since experiencing domain shifts during test-time is inevitable in practice,
test-time adaption (TTA) continues to adapt the model after deployment.
Recently, the area of continual and gradual test-time adaptation (TTA) emerged.
In contrast to standard TTA, continual TTA considers not only a single domain
shift, but a sequence of shifts. Gradual TTA further exploits the property that
some shifts evolve gradually over time. Since in both settings long test
sequences are present, error accumulation needs to be addressed for methods
relying on self-training. In this work, we propose and show that in the setting
of TTA, the symmetric cross-entropy is better suited as a consistency loss for
mean teachers compared to the commonly used cross-entropy. This is justified by
our analysis with respect to the (symmetric) cross-entropy's gradient
properties. To pull the test feature space closer to the source domain, where
the pre-trained model is well posed, contrastive learning is leveraged. Since
applications differ in their requirements, we address several settings,
including having source data available and the more challenging source-free
setting. We demonstrate the effectiveness of our proposed method 'robust mean
teacher' (RMT) on the continual and gradual corruption benchmarks CIFAR10C,
CIFAR100C, and Imagenet-C. We further consider ImageNet-R and propose a new
continual DomainNet-126 benchmark. State-of-the-art results are achieved on all
benchmarks.

本研究提出了一种基于简洁的对称交叉熵损失与对比学习的鲁棒性平均教师方法（RMT），可以在测试时自适应地应对连续和逐渐变化的数据分布转移，实验结果表明，在多个基准测试集上达到了最先进的结果。