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TODO.md

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+# TODO
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+1. Greedy forward selection for the dim search. Random K=20 subsets plateau at AUC 0.74 because of the birthday-problem of dim collisions. Greedy starts with the single best dim, adds the dim that most reduces residual error given the prior picks, repeats. Same wall-clock compute, expected AUC ~0.92 instead of 0.86 because the search no longer wastes trials on redundant dims. Closed-form forward stagewise regression is the strict version.
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+2. Non-linear dim discovery. Currently ridge over raw features. Adding pairwise products (dim_i · dim_j) as candidate features captures multiplicative relationships that linear ridge cannot. The dim search then operates over a 768 + 768·767/2 ≈ 295K candidate-feature space instead of 768.
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+3. Joint discovery for fg+depth+normal. Right now the fg head and depth head are searched independently. Co-discovered: each candidate dim subset is scored on a multi-task loss (foreground AUC + depth RMSE + per-patch surface normal cosine). The optimal subset is rarely the union of two independently-discovered subsets.
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+4. Per-cluster heads. After k-means produces 8 modes, run discovery once per mode. Each mode gets its own dim-subset and ridge coefficients. Adapts to "this is a thin wall-mounted thing" vs "this is a chunky furniture piece" without labels.
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+5. Multi-resolution discovery. Cache features at 384 (24x24 patches) AND 768 (48x48) AND 1024 (64x64) and search over the union. Different scales surface different dims.
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+6. Boundary-aware OBB fit. The current OBB fit takes percentile extents on a point cloud. Instead, find depth discontinuities in image space (large depth gradient) and use those as face-edge constraints. Anchors box faces to actual object boundaries instead of the percentile of a noisy point set.
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+7. Yaw priors from k-means modes. The size priors gave each cluster a (w, h, d). The cluster also has a typical yaw distribution. Wall-mounted clusters skew toward yaw approximately 0 or pi/2 (axis-aligned with walls); freestanding clusters are uniform. Add per-cluster yaw priors and regularize.
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+8. Test-time random-hypothesis search. Brute-force evolutionary search per image. Sample many candidate boxes, score each by (a) fraction of hypothesized-interior pixels that pass the fg threshold, (b) consistency of hypothesized-interior depths with predicted depth from the head, (c) face-edge alignment with depth discontinuities. Keep the best hypothesis. No global shape priors; the image's own evidence drives the search. This is the strictest extension of the 1-param classifier's discovery methodology, the search happens at test time per image, not at train time per dataset.
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+9. 9-DoF box discovery. CA-1M has many non-gravity-aligned boxes. The 7-DoF assumption costs accuracy on those. A single extra discovered head outputting "tilt magnitude" plus a small extension to the OBB fit lifts the floor on tilted objects.
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+10. mAP scoring instead of class-agnostic IoU greedy match. Boxer reports mAP at IoU thresholds [0.05, 0.5]; that's what the field uses. Our current numbers underweight the high-IoU tail. A proper mAP score would be directly comparable to Boxer's 0.43.
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+11. Cross-scene held-out. Discovery and eval both ran on the same 4 scenes. Holding out one scene from discovery and scoring on it tells us what generalizes vs what's scene-specific.
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+12. More CA-1M scenes. We've been running on one scene's eval. Aggregating across 4-10 scenes gives a real distribution.