per-scene optimization

**Per-scene optimization** is the **training paradigm where a separate neural representation is optimized for each individual scene** - it emphasizes scene-specific quality rather than cross-scene generalization. **What Is Per-scene optimization?** - **Definition**: Model parameters are fit from scratch or fine-tuned for one target scene. - **Typical Use**: Classic NeRF pipelines optimize independently per capture sequence. - **Benefit**: Can produce very high fidelity for the trained scene. - **Cost**: Requires substantial compute and time per new scene. **Why Per-scene optimization Matters** - **Quality Ceiling**: Scene-specific fitting can outperform generic feed-forward models. - **Research Baseline**: Provides strong reference quality for evaluating faster methods. - **Control**: Allows tailored hyperparameters for unique scene characteristics. - **Scalability Limit**: Not ideal for large-scale deployment across many scenes. - **Motivation**: Drove development of accelerated methods such as Instant NGP and Gaussian splatting. **How It Is Used in Practice** - **Initialization**: Use good pose estimates and normalized scene scale before optimization. - **Budget Planning**: Set convergence criteria to avoid unnecessary long-tail training. - **Use Case Fit**: Reserve per-scene optimization for high-value or offline rendering tasks. Per-scene optimization is **a high-quality but compute-intensive reconstruction strategy** - per-scene optimization is best when maximum fidelity is required and throughput is secondary.