BEEP3D: Box-Supervised End-to-End Pseudo-Mask Generation for 3D Instance Segmentation

• URL: http://arxiv.org/abs/2510.12182v1
• Date: Tue, 14 Oct 2025 06:23:18 GMT
• Title: BEEP3D: Box-Supervised End-to-End Pseudo-Mask Generation for 3D Instance Segmentation
• Authors: Youngju Yoo, Seho Kim, Changick Kim,
• Abstract summary: 3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations.<n>Box-level annotations inherently introduce ambiguity in overlapping regions, making accurate point-to-instance assignment challenging.<n>Recent methods address this ambiguity by generating pseudo-masks through training a dedicated pseudo-labeler in an additional training stage.<n>We propose BEEP3D-Box-supervised End-to-End Pseudo-mask generation for 3D instance segmentation.
• Score: 28.97274092946373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations, resulting in substantial annotation costs and labor overhead. To mitigate this, box-level annotations have been explored as a weaker but more scalable form of supervision. However, box annotations inherently introduce ambiguity in overlapping regions, making accurate point-to-instance assignment challenging. Recent methods address this ambiguity by generating pseudo-masks through training a dedicated pseudo-labeler in an additional training stage. However, such two-stage pipelines often increase overall training time and complexity, hinder end-to-end optimization. To overcome these challenges, we propose BEEP3D-Box-supervised End-to-End Pseudo-mask generation for 3D instance segmentation. BEEP3D adopts a student-teacher framework, where the teacher model serves as a pseudo-labeler and is updated by the student model via an Exponential Moving Average. To better guide the teacher model to generate precise pseudo-masks, we introduce an instance center-based query refinement that enhances position query localization and leverages features near instance centers. Additionally, we design two novel losses-query consistency loss and masked feature consistency loss-to align semantic and geometric signals between predictions and pseudo-masks. Extensive experiments on ScanNetV2 and S3DIS datasets demonstrate that BEEP3D achieves competitive or superior performance compared to state-of-the-art weakly supervised methods while remaining computationally efficient.

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