Hands-on Practice: Common Tools and Workflows

High-quality data capture is the key to 3DGS success. Shoot around the object 360 degrees, every 10-15 degrees, at multiple height levels (top-down, eye-level, low-angle), ensuring each surface is covered by at least 3 viewpoints. Camera settings: use manual focus to prevent focus jumping, fix exposure parameters for consistent brightness, shutter speed no slower than 1/250s to avoid motion blur, and keep ISO low to reduce noise. Lighting: choose diffuse light (overcast day, indoor softboxes), avoid strong shadows and highlights.

Common mistakes and solutions: motion blur (use tripod or stabilizer, increase shutter speed); reflective surfaces (use polarizer, adjust angle to avoid reflections); plain-colored surfaces (add marker stickers to increase texture features); moving objects (wait for stillness or use masking in post-processing). Capture count guidelines: small objects 50-100 shots, rooms 100-200 shots, building exteriors 200-500 shots. The principle is more is better — you can always filter in post.

Luma AI offers zero-barrier entry: fully cloud-based, just shoot a slow-orbit video with your phone, upload, and get results in 10-30 minutes with PLY export support. Polycam suits mobile users wanting more control, with LiDAR assistance (iPhone/iPad Pro), basic editing, and local or cloud processing options. Kiri Engine is optimized for object scanning with background separation and photo/video dual-mode capture. These tools are perfect for quick trials and beginners, but don't allow custom training parameters.

COLMAP is an open-source SfM tool for estimating camera positions from photos and generating sparse point clouds — the first step in 3DGS training. Basic workflow: feature extraction → feature matching → sparse reconstruction → export text format for 3DGS. Common issues: reconstruction failure (poor image quality or insufficient feature points), incorrect camera parameters (manually specify camera model or intrinsics), sparse point cloud (increase feature point count).

Nerfstudio is a unified neural radiance field training framework supporting multiple methods including 3DGS. Use ns-process-data to process raw images or video (automatically runs COLMAP), then ns-train splatfacto to start training with a real-time web viewer for monitoring, and ns-export gaussian-splat to export the PLY file. Typical training completes in 30-60 minutes. The original INRIA implementation is the official reference, fully consistent with the paper and best performance, but lacks visualization tools. gsplat is Nerfstudio's efficient Python library suited for custom development.

When COLMAP fails, first check image quality (blur, underexposure), then increase feature point count. When training doesn't converge, verify SfM results confirm reasonable camera positions and adjust learning rates. For 'floaters' (airborne Gaussian points), increase pruning thresholds or manually clean with SuperSplat. For out-of-memory errors, reduce image resolution or training image count. Advanced tips: use block-based processing for large scenes; accept limitations for reflective surfaces while using polarizers; improve quality by extending training iterations to 50,000-100,000 while monitoring loss curve convergence.