The field of 3D object reconstruction and anomaly detection is rapidly advancing with the development of innovative methods that leverage multimodal collaboration, debiased feature augmentation, and effective Gaussian management. These approaches aim to improve the accuracy and efficiency of 3D object reconstruction, anomaly detection, and semantic segmentation. Notably, the use of multimodal collaboration learning, disentangled representation learning, and diffusion-based methods has shown promising results in achieving state-of-the-art performance in various tasks.

Some noteworthy papers in this area include: MCL-AD, which introduces a novel framework for zero-shot 3D anomaly detection using multimodal collaboration learning. Learning by Imagining, which proposes a debiased feature augmentation approach for compositional zero-shot learning. Effective Gaussian Management, which presents a novel densification strategy for high-fidelity object reconstruction. A-TDOM, which introduces a near real-time TDOM generation method based on On-the-Fly 3DGS optimization. Few to Big, which proposes a prototype expansion network for point cloud few-shot semantic segmentation. Dream3DAvatar, which presents a text-controllable two-stage framework for 3D avatar generation. StyleSculptor, which introduces a zero-shot style-controllable 3D asset generation approach. FMGS-Avatar, which proposes a mesh-guided 2D Gaussian splatting method for 3D monocular avatar reconstruction. Seeing 3D Through 2D Lenses, which presents a cross-modal geometric rectification framework for 3D few-shot class-incremental learning.