UNION: Unsupervised Multi-Modal 3D Object Detection using Appearance-Based Pseudo-Classes for Improved Training Efficiency
UNION, a novel unsupervised 3D object detection method, leverages the joint strengths of LiDAR and camera data to achieve state-of-the-art performance by effectively distinguishing between static foreground and background objects, eliminating the need for computationally expensive self-training.
Unsupervised 3D Object Detection with Uncertainty-Aware Bounding Boxes: A Novel Approach for Improved Accuracy
Inaccurate pseudo bounding boxes hinder unsupervised 3D object detection, but a novel uncertainty-aware framework, UA3D, mitigates this by estimating and regularizing uncertainty at the coordinate level, leading to substantial performance improvements.
Unsupervised 3D Object Detection Using Commonsense Prototypes
This paper introduces a Commonsense Prototype-based Detector (CPD) that can perform accurate unsupervised 3D object detection without human annotations. CPD constructs high-quality commonsense prototypes to refine pseudo-labels and guide the network convergence, significantly outperforming state-of-the-art unsupervised 3D detectors.
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