TRELLIS.2 is a research codebase for state-of-the-art image-to-3D generation using a novel sparse voxel representation (O-Voxel) and flow matching with 4B parameters. It provides full training and inference pipelines for generating high-fidelity 3D meshes with PBR materials from single images. The repository includes custom CUDA extensions for voxel processing, a flexible sparse tensor library, and comprehensive dataset tooling for large-scale 3D asset curation (Objaverse-XL, Sketchfab). While primarily a research project with training code and data pipelines, it also offers Gradio web demos and Python APIs for inference. The codebase demonstrates strong engineering in its modular design (pipelines, mixins, config-driven training) but suffers from some research-typical issues like code duplication, hardcoded paths, and fragile dependency management.