While numerous 3D detection works leverage the complementary relationship between RGB
images and point clouds, developments in the broader framework of semi-supervised object …

Semi-supervised object detection is crucial for 3D scene understanding, efficiently
addressing the limitation of acquiring large-scale 3D bounding box annotations. Existing …

Advanced 3D object detection methods usually rely on large-scale, elaborately labeled
datasets to achieve good performance. However, labeling the bounding boxes for the 3D …

3D object detection is an important task in computer vision. Most existing methods require a
large number of high-quality 3D annotations, which are expensive to collect. Especially for …

The performance of existing point cloud-based 3D object detection methods heavily relies
on large-scale high-quality 3D annotations. However, such annotations are often tedious …

A crucial task in scene understanding is 3D object detection, which aims to detect and
localize the 3D bounding boxes of objects belonging to specific classes. Existing 3D object …

In this paper, we study the problem of semi-supervised 3D object detection, which is of great
importance considering the high annotation cost for cluttered 3D indoor scenes. We resort to …

Pseudo-Labeling (PL) is a critical approach in semi-supervised 3D object detection (SSOD).
In PL, delicately selected pseudo-labels, generated by the teacher model, are provided for …

Abstract State-of-the-art 3D object detectors are usually trained on large-scale datasets with
high-quality 3D annotations. However, such 3D annotations are often expensive and time …

Abstract 3D object detection is an important yet demanding task that heavily relies on difficult
to obtain 3D annotations. To reduce the required amount of supervision, we propose …