AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction

• URL: http://arxiv.org/abs/2412.18255v1
• Date: Tue, 24 Dec 2024 08:12:31 GMT
• Title: AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction
• Authors: Pufan Zou, Shijia Zhao, Weijie Huang, Qiming Xia, Chenglu Wen, Wei Li, Cheng Wang,
• Abstract summary: We propose a novel label-free learning method, Adaptive Label Correction (AdaCo), for 3D semantic segmentation.<n>AdaCo incorporates the Cross-modal Label Generation Module (CLGM), updating and adjusting the noisy samples within this supervision iteratively during training.<n>Our proposed AdaCo can effectively mitigate the performance limitations of label-free learning networks in 3D semantic segmentation tasks.
• Score: 14.51758173099208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks. However, their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor conditions, and the abundance of unknown objects. In this work, we propose a novel label-free learning method, Adaptive Label Correction (AdaCo), for 3D semantic segmentation. AdaCo first introduces the Cross-modal Label Generation Module (CLGM), providing cross-modal supervision with the formidable interpretive capabilities of the VFMs. Subsequently, AdaCo incorporates the Adaptive Noise Corrector (ANC), updating and adjusting the noisy samples within this supervision iteratively during training. Moreover, we develop an Adaptive Robust Loss (ARL) function to modulate each sample's sensitivity to noisy supervision, preventing potential underfitting issues associated with robust loss. Our proposed AdaCo can effectively mitigate the performance limitations of label-free learning networks in 3D semantic segmentation tasks. Extensive experiments on two outdoor benchmark datasets highlight the superior performance of our method.

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