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Found 8 papers, 2 papers with code
ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors
Given an unseen object and a reference pose-object pair, we optimise for the object-aware pose that is closest in the feature space to the reference pose.
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation.
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
We also demonstrate the feasibility of a more efficient training strategy.
Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels
In this paper, we propose an unsupervised domain adaptation method for deep point cloud representation learning.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
Ranked #1 on
3D Point Cloud Data Augmentation
on ModelNet40
Source-Free Few-Shot Domain Adaptation
Test-time adaptation of pre-trained source models with streaming unlabelled target data is an attractive setting that protects the privacy of source data, but it has mini-batch size and class-distribution requirements on the streaming data which might not be desirable in practice.
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
We use a U-Net style 3D sparse convolution network to extract features for each frame's LiDAR point-cloud.
