Training Tutorial For 3D Gaussian Splatting

A 3DGS trainer starts with positions, often initialized from sparse SfM points, then learns Gaussian scale, rotation, opacity, color, and sometimes spherical harmonic coefficients for view-dependent appearance. Each step renders one or more training views, compares them with target images, backpropagates image loss, and updates the Gaussian parameters.

The important difference from many NeRF methods is that 3DGS trains on full images and maintains an explicit primitive set. The trainer can clone, split, and prune Gaussians during optimization. This density control is powerful, but it can also amplify bad poses, bad masks, or areas with inconsistent images.

Use the original Inria implementation when you want a reference baseline and maximum comparability with the original paper. Use gsplat when you want a modern Python training stack with efficient rasterization and examples that fit COLMAP captures. Use Nerfstudio Splatfacto when you want an integrated viewer, data processing commands, and a broader NeRF/3DGS ecosystem.

Use OpenSplat when you need a portable native implementation or mixed hardware support. Use Brush or Postshot when you want an interactive desktop workflow and do not want to operate mostly through Python scripts. The best trainer is often the one whose output, hardware assumptions, and debugging tools fit your project rather than the one with the highest benchmark number.

Good dataset preparation is boring and decisive. Keep images, camera files, sparse points, and output directories separate. If your trainer expects COLMAP, preserve the sparse model layout. If your trainer expects nerfstudio data, use ns-process-data and confirm that transforms, image paths, and point initialization are present.

Image resolution is a tradeoff. Higher resolution can improve details but increases GPU memory and training time. A practical workflow is to train a fast preview at lower resolution, inspect geometry and artifacts, then train a higher-quality version only after the input is proven. For large scenes, image downsampling and tile or scene splitting may matter more than trainer choice.

Most users should begin with the default schedule of the chosen trainer, then tune only after seeing a specific failure. Key settings include image scale, total iterations, densification start and stop, opacity reset, pruning thresholds, spherical harmonic degree, learning rates, and whether camera pose refinement is allowed.

If training produces many floaters, the cause may be bad alignment, transient objects, overly aggressive densification, or opacity that never gets pruned. If the scene is blurry, the cause may be low image resolution, poor alignment, under-training, or overly strong pruning. If the file is huge, reduce unnecessary density after getting a clean source model rather than prematurely lowering quality.

Do not wait until the final PLY to inspect training. Early previews reveal whether the scene is coherent. Within the first part of training, major shapes should appear in the right place. If the scene is duplicated, folded, or exploding, stop and debug alignment or dataset structure instead of burning more GPU time.

A good monitoring routine checks the live viewer, random held-out views, file size, Gaussian count, and GPU memory. If the trainer supports evaluation, keep a small set of images for validation. Metrics are useful, but visual inspection from novel viewpoints catches failure modes that PSNR may hide.

Export a full-quality PLY or trainer-native checkpoint as the source artifact before conversion. PLY is large, but it is the common interchange format for editing, compression, and many viewers. Runtime formats such as SPZ, SOG, SPLAT, and KSPLAT are useful for delivery, but they may be lossy or viewer-specific.

Keep a small manifest with trainer name, commit or version, dataset path, image scale, iterations, output model, and any non-default parameters. This turns a one-off capture into a reproducible asset. It also makes it easier to compare a retrain against the original output later.