多智能体协同选点重建，提升三维场景精度与覆盖率。
COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction
- 共享策略优化视角分配，基于地图融合决策无通信
- 相同感知预算下，重建准确率最高提升54%，覆盖率达49%增长
- 适合需要高效多机协同的三维重建场景
主动3D重建需在有限感知预算下选择信息量大的观测视角。多智能体环境下，冗余观测和空间聚集会显著降低重建质量。我们提出COLMAR，一种面向多智能体主动3D重建的协同视角策略学习框架。将视角分配建模为以地图为中心的共享策略优化，并引入重构感知目标，促进重叠感知覆盖、团队级发现及避障探索。通过增量重建更新生成密集反馈，使探索行为与下游几何质量对齐。采用参数共享的近端策略优化（PPO）训练，部署时独立选择动作，仅依赖融合后的团队地图，无需智能体间通信。选定视角使用3D高斯喷溅（3DGS）进行高保真光度评估。在GLEAM和Replica数据集上的实验表明，相比启发式与非协作基线，性能持续提升，相同感知预算下重建准确率最高提高54%，覆盖率提升49%。
原文摘要 · Abstract (English)
Active 3D reconstruction requires selecting informative viewpoints under limited sensing budgets. In multi-agent settings, coordination inefficiencies such as redundant observations and spatial clustering can significantly reduce reconstruction quality. We present COLMAR, a cooperative view policy learning framework for multi-agent active 3D reconstruction. COLMAR formulates viewpoint allocation as a shared policy optimization over map-centric observations and introduces a reconstruction-aware objective that promotes overlap-aware coverage, team-level discovery, and collision-safe exploration. Dense feedback derived from incremental reconstruction updates aligns exploration behavior with downstream geometric quality. The policy is trained using parameter-sharing Proximal Policy Optimization (PPO) with independent per-agent action selection at deployment, conditioned on a fused team map and without inter-agent message passing for decision making. Selected viewpoints are then reconstructed with 3D Gaussian Splatting (3DGS) for high-fidelity photometric evaluation. Experiments on GLEAM and Replica demonstrate consistent improvements over heuristic and non-cooperative baselines, achieving up to 54% higher reconstruction accuracy and 49% greater coverage under matched sensing budgets.
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