Siggraph experiments

SIGGRAPH Experiments

SIGGRAPH acceptance turns on evidence proportional to a graphics claim: a technique that claims to be faster must be timed against a real baseline on stated hardware; one that claims higher quality must be compared, quantitatively and visually, against the strongest prior method. This skill matches evaluation to claim shape and pre-empts the domain-expert reviewer's first objections. Anchor policy to resources/official-source-map.md.

Match evidence to the claim

Claim shape	Evidence the reviewer expects	Common failure
"Higher quality"	Head-to-head vs SOTA with a metric (PSNR/SSIM/LPIPS/FLIP; Hausdorff/normal error for geometry) + side-by-side visuals + video	Only one's own results shown; no baseline
"Faster / real-time"	Wall-clock vs baseline at equal quality, with GPU/CPU, driver, resolution	Timing at unequal quality; no hardware stated
"More general / robust"	Results across a broad, non-cherry-picked scene set incl. hard cases	Works only on the paper's three easy inputs
"New capability"	Demonstrations prior methods provably cannot produce	Capability asserted, not shown against a method that fails
"Perceptually better"	A user/perceptual study with enough participants and a valid protocol	"Looks better" with no study

The comparison is the evaluation

In graphics, the head-to-head comparison against the strongest prior method is not optional:

• Reproduce baselines faithfully. Use authors' code and recommended settings; if you must reimplement, say so and match their reported numbers where possible. A weakened baseline is the objection that sinks the paper.
• Equalize conditions. Same scene, viewpoint, lighting, sample/time budget. When you give yourself or the baseline an advantage, disclose it.
• Show the comparison both ways — a metric table and a visual side-by-side (still + video); numbers and pixels persuade different reviewers.
• Include the cases where you lose. Bounding your method's regime is credibility, not weakness.

Metrics, honestly

• Images: PSNR/SSIM for fidelity, LPIPS/FLIP for perceptual difference; state the reference and the region of interest. No single metric is sufficient — report several and show the images.
• Geometry: Hausdorff / mean surface distance, normal/curvature error, element quality; state the alignment and units.
• Simulation/animation: energy/momentum behavior, stability under time-step, constraint residuals; a plot over time, not a single frame.
• Report variance where results are stochastic (multiple seeds/runs), and never compare at unequal sample counts or resolutions without saying so.

Performance and timing are first-class

Timings are claims a reviewer will check:

• Report hardware (GPU/CPU model, memory, driver), resolution/scene size, and settings for every timing.
• Break down where time goes (preprocess vs per-frame vs per-sample) so the claim is auditable.
• Compare speed at matched quality — "faster" at lower quality is not faster.

Perceptual and user studies

When the claim is about perceived quality or usability:

• Pre-register the protocol; report participant count, task, stimuli, and the statistic (with a correction for multiple comparisons where relevant).
• Use a valid design (two-alternative forced choice, ranking, or a calibrated scale); report effect size and confidence intervals, not just significance.
• Put stimuli and raw responses in the supplemental for reproducibility.

Ablations isolate the contribution

• Turn off each component in turn and show the quality/speed cost — this proves the contribution is the part you claim, not an incidental engineering detail.
• For learning-based methods, ablate architecture, loss terms, and data; run a contamination check so test scenes are not in training.
• Key ablation rows go in the body; the full grid goes to the supplemental (see siggraph-supplementary).

Anti-patterns

• No comparison to the obvious strongest baseline.
• Timings with no hardware, or "faster" at unequal quality.
• A single cherry-picked scene standing in for generality.
• One metric asserted as quality with no images shown.
• A perceptual claim with no study, or a study with too few participants to support it.

Output format

[Claim -> evidence] each claim matched to comparison/metric/timing/study? yes/no
[Baselines] strongest prior method compared, faithfully, at equal conditions? yes/no
[Metrics] appropriate metrics + visuals/video for each quality claim? yes/no
[Timing] hardware/resolution/settings reported, matched-quality? yes/no
[Ablations] each component isolated; contamination checked (if learned)? yes/no
[Gaps] <ordered, with the reviewer objection each closes>