E-RayZer is a self-supervised 3D reconstruction / spatial visual pre-training research codebase. In practice, it takes unordered multi-view RGB images, estimates camera poses via a transformer-based PoseEstimator, constructs a per-pixel 3D Gaussian scene representation, and renders novel views via differentiable Gaussian splatting (gsplat). The repository includes a Gradio demo, a reusable inference engine, training/evaluation drivers for the DL3DV dataset, and benchmark split tooling for ScanNet++ and BlendedMVS. The code is organized into model modules (transformer encoder/decoder, pose estimator, Gaussian upsampler/renderer), utility layers (camera geometry, loss functions including VGG19 perceptual loss), and data-preparation scripts (Hugging Face downloader, OpenCV list conversion, covisibility/DINOv2 statistics, manifest checking). Training uses curriculum view selection, mixed precision, DDP, gradient accumulation, and configurable loss composition; evaluation reports PSNR and pose RPA metrics. Although the dominant character is research/data engineering, the repo is hybrid: it ships application-style entry points (gradio_app.py, app_core/engine.py) as well as data pipelines and model training scripts. The public training path is limited to DL3DV; the ScanNet++ and BlendedMVS benchmark scripts are historical protocol references that rely on internal S3 tooling. Reproducibility is moderate: DL3DV setup is documented and scripted, but the custom CUDA renderutils plugin, third-party gsplat submodule, external VGG weights, and curated download URLs make the environment fragile. Model weights are separately licensed under Adobe's non-commercial research license, while code is MIT.