A survey of label-efficient deep learning for 3D point clouds
In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
Diff3DETR: Agent-Based Diffusion Model for Semi-supervised 3D Object Detection
Abstract 3D object detection is essential for understanding 3D scenes. Contemporary
techniques often require extensive annotated training data, yet obtaining point-wise …
techniques often require extensive annotated training data, yet obtaining point-wise …
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection
3D object detection aims to recover the 3D information of concerning objects and serves as
the fundamental task of autonomous driving perception. Its performance greatly depends on …
the fundamental task of autonomous driving perception. Its performance greatly depends on …
Exploring Relational Knowledge for Source-free Domain Adaptation
Y Ma, L Chai, S Tu, Q Wang - … on Circuits and Systems for Video …, 2024 - ieeexplore.ieee.org
Standard domain adaptation methods require access to both source and target data.
However, sharing source data is often impractical in real-world scenarios due to data privacy …
However, sharing source data is often impractical in real-world scenarios due to data privacy …
ALPI: Auto-Labeller with Proxy Injection for 3D Object Detection using 2D Labels Only
3D object detection plays a crucial role in various applications such as autonomous
vehicles, robotics and augmented reality. However, training 3D detectors requires a costly …
vehicles, robotics and augmented reality. However, training 3D detectors requires a costly …