Panoramic 3D reconstruction is essential for immersive scene understanding in robotics, AR, and autonomous driving. However, most existing methods are designed for pinhole images and generalize poorly to 360° inputs due to the scarcity of panoramic training data and the high cost of retraining. We present Pano3R, the first training-free framework for panoramic 3D reconstruction that adapts existing pinhole-based models without any retraining. Pano3R consists of two stages. Specifically, the pre-processing stage applies a position-aware pairing strategy to decompose each panorama into a minimal set of perspective views. These views are selected to ensure sufficient co-visible regions while minimizing the number of projections. The test-time optimization stage incorporates a pose-prior-guided global alignment strategy to improve global consistency and mitigate accumulated errors. Our method enables accurate 360° reconstruction under both single- and multi-view input conditions. Extensive experiments demonstrate that Pano3R consistently improves reconstruction accuracy and pose estimation quality, establishing a strong and practical benchmark for training-free panoramic 3D reconstruction.

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using human data. Although such agents can be obtained through self-play training, they can suffer significantly from the distributional shift when paired with unencountered partners, such as humans. In this paper, we propose Maximum Entropy Population-based training (MEP) to mitigate such distributional shift. In MEP, agents in the population are trained with our derived Population Entropy bonus to promote the pairwise diversity between agents and the individual diversity of agents themselves. After obtaining this diversified population, a common best agent is trained by paring with agents in this population via prioritized sampling, where the prioritization is dynamically adjusted based on the training progress. We demonstrate the effectiveness of our method MEP, with comparison to Self-Play PPO (SP), Population-Based Training (PBT), Trajectory Diversity (TrajeDi), and Fictitious Co-Play (FCP) in both matrix game and Overcooked game environments, with partners being human proxy models and real humans. A supplementary video showing experimental results is available at https://youtu.be/Xh-FKD0AAKE.