PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding

• URL: http://arxiv.org/abs/2604.04933v1
• Date: Mon, 06 Apr 2026 17:59:58 GMT
• Title: PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding
• Abstract summary: We propose a Test-time Adaptation framework that generates input-aware network parameters for scene-level point clouds.<n>PointTPA achieves 78.4% mIoU on ScanNet validation, surpassing existing parameter-efficient fine-tuning methods.
• Score: 51.98954431091292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene-level point cloud understanding remains challenging due to diverse geometries, imbalanced category distributions, and highly varied spatial layouts. Existing methods improve object-level performance but rely on static network parameters during inference, limiting their adaptability to dynamic scene data. We propose PointTPA, a Test-time Parameter Adaptation framework that generates input-aware network parameters for scene-level point clouds. PointTPA adopts a Serialization-based Neighborhood Grouping (SNG) to form locally coherent patches and a Dynamic Parameter Projector (DPP) to produce patch-wise adaptive weights, enabling the backbone to adjust its behavior according to scene-specific variations while maintaining a low parameter overhead. Integrated into the PTv3 structure, PointTPA demonstrates strong parameter efficiency by introducing two lightweight modules of less than 2% of the backbone's parameters. Despite this minimal parameter overhead, PointTPA achieves 78.4% mIoU on ScanNet validation, surpassing existing parameter-efficient fine-tuning (PEFT) methods across multiple benchmarks, highlighting the efficacy of our test-time dynamic network parameter adaptation mechanism in enhancing 3D scene understanding. The code is available at https://github.com/H-EmbodVis/PointTPA.

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