Mingwei Xing

Mingwei Xing, Xinliang Wang, Yifeng Shi

This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply complex, architecture-specific designs on top of the generic pipeline of image feature extraction $\rightarrow$ multi-view interaction $\rightarrow$ feature decoding. However, constrained by the scale bottleneck of 3D training data and the low-pass filtering effect of deep networks, these methods still fall short in cross-domain generalization and high-frequency geometric fidelity. To address these problems, we propose AdaptSplat, which demonstrates that without complex component engineering, introducing a single adapter of only 1.5M parameters into the generic architecture is sufficient to achieve superior performance. Specifically, we design a lightweight Frequency-Preserving Adapter (FPA) that extracts direction-aware high-frequency structural priors from the shallow features of a powerful vision foundation model backbone, and seamlessly integrates them into the generic pipeline via high-frequency positional encodings and adaptive residual modulation. This effectively compensates for the high-frequency attenuation caused by over-smoothing in deep features, improving the fitting accuracy of Gaussian primitives on complex surfaces and sharp boundaries. Extensive experiments demonstrate that AdaptSplat achieves state-of-the-art feed-forward reconstruction performance on multiple standard benchmarks, with stable generalization across domains. Code available at: https://github.com/xmw666/AdaptSplat.

Yao Wu, Mingwei Xing, Yachao Zhang et al.

In cross-modal unsupervised domain adaptation, a model trained on source-domain data (e.g., synthetic) is adapted to target-domain data (e.g., real-world) without access to target annotation. Previous methods seek to mutually mimic cross-modal outputs in each domain, which enforces a class probability distribution that is agreeable in different domains. However, they overlook the complementarity brought by the heterogeneous fusion in cross-modal learning. In light of this, we propose a novel fusion-then-distillation (FtD++) method to explore cross-modal positive distillation of the source and target domains for 3D semantic segmentation. FtD++ realizes distribution consistency between outputs not only for 2D images and 3D point clouds but also for source-domain and augment-domain. Specially, our method contains three key ingredients. First, we present a model-agnostic feature fusion module to generate the cross-modal fusion representation for establishing a latent space. In this space, two modalities are enforced maximum correlation and complementarity. Second, the proposed cross-modal positive distillation preserves the complete information of multi-modal input and combines the semantic content of the source domain with the style of the target domain, thereby achieving domain-modality alignment. Finally, cross-modal debiased pseudo-labeling is devised to model the uncertainty of pseudo-labels via a self-training manner. Extensive experiments report state-of-the-art results on several domain adaptive scenarios under unsupervised and semi-supervised settings. Code is available at https://github.com/Barcaaaa/FtD-PlusPlus.

Mingwei Xing, Xinliang Wang, Yifeng Shi

The generalization of 3D deep learning across multiple domains remains limited by the limited scale of existing datasets and the high heterogeneity of multi-source point clouds. Point clouds collected from different sensors (e.g., LiDAR scans and mesh-derived point clouds) exhibit substantial discrepancies in density and noise distribution, resulting in negative transfer during multi-domain fusion. Most existing approaches focus exclusively on either domain-aware or domain-general features, overlooking the potential synergy between them. To address this, we propose DoReMi (Domain-Representation Mixture), a Mixture-of-Experts (MoE) framework that jointly models Domain-aware Experts branch and a unified Representation branch to enable cooperative learning between specialized and generalizable knowledge. DoReMi dynamically activates domain-aware expert branch via Domain-Guided Spatial Routing (DSR) for context-aware expert selection and employs Entropy-Controlled Dynamic Allocation (EDA) for stable and efficient expert utilization, thereby adaptively modeling diverse domain distributions. Complemented by a frozen unified representation branch pretrained through robust multi-attribute self-supervised learning, DoReMi preserves cross-domain geometric and structural priors while maintaining global consistency. We evaluate DoReMi across multiple 3D understanding benchmarks. Notably, DoReMi achieves 80.1% mIoU on ScanNet Val and 77.2% mIoU on S3DIS, demonstrating competitive or superior performance compared to existing approaches, and showing strong potential as a foundation framework for future 3D understanding research. The code will be released soon.