RESEARCH_BACKLOG.md

# Simam3D research backlog

This backlog separates testable hypotheses from implemented behavior. None of
the ideas below should be described as a result until an experiment produces a
held-out metric and a provenance-validated artifact.

## 1. Evidence-guided hidden-side completion

**Hypothesis:** conditioning a completion model on visible depth, confidence,
lighting direction, and the user's scene hypothesis reduces implausible
back-side geometry compared with image-only completion.

**Experiment:** hold out one or more views from a calibrated object capture;
compare image-only, depth-only, and depth-plus-evidence conditions. Report
held-out depth error, silhouette IoU, novel-view perceptual error, and failure
rate on occluded markings. The user's hypothesis must be logged as metadata,
never treated as ground truth.

## 2. Uncertainty-guided view acquisition

**Hypothesis:** selecting the next camera from the largest uncertainty-weighted
surface gap improves coverage per captured view over a fixed azimuth schedule.

**Experiment:** run both policies on the same synthetic and real multi-view
sets. Measure coverage, reprojection consistency, held-out depth, and points
with contributions from at least two source views after each added frame.

## 3. Photometric light-field consistency

**Hypothesis:** a depth-normal light estimate can reject inconsistent Gaussian
colors before optimization, improving relit-view stability without changing
geometry.

**Experiment:** compare no filtering, image-space light filtering, and
depth-normal filtering under controlled lighting. Report color error under
held-out relighting and geometry preservation. The current detector is only a
heuristic diagnostic and is not a lighting reconstruction result.

## 4. Cross-modal reference-to-avatar handoff

**Hypothesis:** a capture manifest containing identity-safe labels, view hashes,
coverage, and explicit consent improves reproducibility of reference-conditioned
video generation compared with an unstructured prompt and image folder.

**Experiment:** compare reference sets using the same approved subject and
prompt. Track reference retention, temporal identity drift, pose adherence,
manual artifact review, and disclosure/watermark compliance. Do not publish
identifiable samples without permission.

## 5. Gaussian evidence instead of opaque splats

**Hypothesis:** retaining confidence, source-view pointers, and contribution
bitmasks in the Gaussian initialization makes downstream pruning and debugging
more reliable than an unlabeled point cloud.

**Experiment:** ablate the evidence fields and measure removal of low-confidence
points, source disagreement, memory, render quality, and held-out-view error.
The current implementation provides the evidence fields and deterministic
fusion; it does not yet perform learned Gaussian optimization.

## 6. Calibration-aware fusion gating

**Hypothesis:** combining DA3 camera poses with an explicit view-diversity and
cross-view-support gate reduces false confidence from synthetic orbit priors,
especially when users provide repeated, near-duplicate, or weakly overlapping
views.

**Experiment:** evaluate three fusion modes on calibrated captures: synthetic
orbit priors, native DA3 poses, and native DA3 poses with low-support pruning.
Hold out one view and report reprojection coverage, relative-depth error,
silhouette overlap, and the fraction of retained points supported by two or
more distinct inputs. Include duplicate and near-duplicate controls so a high
overlap score cannot be mistaken for useful view coverage.

## 7. Observed execution evidence (not a quality result)

The hosted v0.4.1 pipeline has completed one-, two-, and three-input synthetic
requests with native DA3 provenance and direct Gaussian export. The controlled
three-input stress test recorded 9.9% multi-view voxel support, while a
duplicate-input request was explicitly labeled `duplicate_input_support`.
These observations motivate the calibration experiment above; they are not
evidence of metric accuracy, unseen-side correctness, or superiority over a
baseline.

## Current acceptance rule

Every completed experiment must include: pinned code and model revisions,
input hashes, runtime/hardware, dataset split, baseline, metrics, failure cases,
and a manifest validated by `validate_manifest.py`. A visually convincing
turntable alone is insufficient evidence for hidden-surface accuracy or a
"perfect digital twin."