Collaborative perception enables connected vehicles to share information, overcoming occlusions and extending the limited sensing range inherent in single-agent (non-collaborative) systems. Existing vision-only methods for 3D semantic occupancy prediction commonly rely on dense 3D voxels, which incur high communication costs, or 2D planar features, which require accurate depth estimation or additional supervision, limiting their applicability to collaborative scenarios. To address these challenges, we propose the first approach leveraging sparse 3D semantic Gaussian splatting for collaborative 3D semantic occupancy prediction. By sharing and fusing intermediate Gaussian primitives, our method provides three benefits: a neighborhood-based cross-agent fusion that removes duplicates and suppresses noisy or inconsistent Gaussians; a joint encoding of geometry and semantics in each primitive, which reduces reliance on depth supervision and allows simple rigid alignment; and sparse, object-centric messages that preserve structural information while reducing communication volume. Extensive experiments demonstrate that our approach outperforms single-agent perception and baseline collaborative methods by +8.42 and +3.28 points in mIoU, and +5.11 and +22.41 points in IoU, respectively. When further reducing the number of transmitted Gaussians, our method still achieves a +1.9 improvement in mIoU, using only 34.6% communication volume, highlighting robust performance under limited communication budgets.

Point set registration is a fundamental task in 3D computer vision. Existing registration approaches mainly focus on either pair-wise or rigid registration. In this paper, we propose a robust group-wise registration method from a probabilistic view and adopt non-rigid transformations to register multiple point sets without bias toward any given set. The proposed method lessens the need of point correspondences by representing each point set as Gaussian Mixture Model and the registration is equivalent to multiple distributions alignment. Closed-form of Jensen-Rényi divergence and L2distance are used as cost functions. We further design a neural network to extract correspondences between raw point sets and Gaussian Mixture Model (GMM) parameters, and recover the optimal diffeomorphic non-rigid transformations from the matched GMM parameters. The proposed method is compared against two well-known probabilistic methods for group-wise point-set registration on several public 2D and 3D datasets. The results demonstrate that our method improves registration accuracy.