--- license: cc-by-4.0 language: - en tags: - 3d-point-clouds - openshape - benchmark-contamination - bmvc-2026 - anonymous pretty_name: "OpenShape Benchmark Contamination — BMVC 2026 (Anonymous)" --- # OpenShape Benchmark Contamination Artifacts (BMVC 2026, Anonymous Release) > **Released anonymously for BMVC 2026 double-blind review.** > Authorship and provenance will be revealed on acceptance. This release accompanies a BMVC 2026 submission analyzing benchmark contamination in 3D representation learning. It contains the **training prune masks, training/eval configs, eval-time per-step metrics, training logs, NN-proxy predictions, and the best-LVIS checkpoint for the counterfactual training runs** used to support the paper's headline claims. ## What's in this release This is split across two anonymous repos: | Repo | Contents | |------|----------| | `datasets//openshape-contamination-axes-data` | masks, configs, metrics, training logs, training code, eval splits, NN-proxy predictions | | `/openshape-contamination-axes-checkpoints` | best_lvis.pt for 5 counterfactual training runs | ## File map (paper claim → file) The paper analyzes how four kinds of training-set "leakage" affect downstream LVIS zero-shot top-1 accuracy. Each run trains an OpenShape PointBERT encoder against a different prune mask of the training corpus, then evaluates on the same held-out LVIS split. The "T1" stack is single-stack (PointBERT-only); "T2-MLP" adds a small text-tower MLP, matching the paper's primary three-stack reported numbers. | Paper claim | Run | Files in this release | |---|---|---| | C43 Δ_total = 14.27 pp full→pruned, headline result | Run-0 vs Run-A vs Run-C (T2-MLP) | `metrics/run_0_t2_mlp_metrics.jsonl`, `metrics/run_c_v3_t2_mlp_metrics.jsonl`, `checkpoints/run_0_t2_mlp_best_lvis.pt`, `checkpoints/run_c_v3_t2_mlp_best_lvis.pt`, `masks/run_0.npy`, `masks/run_C.npy` | | Run-A (52% self-class prune) | Run-A T2-MLP | mask + log + config bundled; T2-MLP checkpoint not available for this release (see "What's NOT here") | | Run-B v2 reframed (2.7% per-item-impact prune) | Run-B v2 T2-MLP | `checkpoints/run_b_v2_t2_mlp_best_lvis.pt`, `metrics/run_b_v2_t2_mlp_metrics.jsonl`, `masks/run_B.npy`, `configs/run_b_v2_t2_mlp_config.yaml` | | Run-C T2-MLP v3 best 45.97 LVIS top-1 | Run-C v3 T2-MLP | `checkpoints/run_c_v3_t2_mlp_best_lvis.pt`, `metrics/run_c_v3_t2_mlp_metrics.jsonl`, `configs/run_c_v3_t2_mlp_config.yaml` | | Run-NN matched-volume NN-axis ablation | Run-NN T1 + T2-MLP | `checkpoints/run_nn_t1_best_lvis.pt`, `checkpoints/run_nn_t2_mlp_best_lvis.pt`, metrics + configs | | Run-0 three-stack baseline | Run-0 T2-MLP | `checkpoints/run_0_t2_mlp_best_lvis.pt`, `metrics/run_0_t2_mlp_metrics.jsonl`, `configs/run_0_t2_mlp_config.yaml` | | Own-caption proxy (Table 1) | NN-proxy LVIS assignments | `predictions/lvis_nn_assignments.parquet` | | Captioner sweep (G4_C, supplementary) | NN-proxy under 4 caption corpora | `predictions/captioner_sweep_per_object.parquet` | | Uni3D replication gap decomposition | per-object preds + decomposition | `predictions/uni3d_per_object_preds.parquet`, `predictions/uni3d_gap_decomp.json` | | LVIS eval split | held-out LVIS objects (UID → class) | `splits/lvis_eval.json` | Full per-row mapping is in `CLAIM_TO_FILE_MAP.md` (copied from the paper supplementary). ## Repo layout ``` masks/ 4 × .npy boolean prune masks (one per run) configs/ Training YAML configs (one per checkpoint) metrics/ Per-step train + per-epoch eval JSONL logs checkpoints/ best_lvis.pt for 5 runs (Model repo only) code/ Training launcher + losses + mask-build scripts predictions/ NN-proxy per-UID predictions + Uni3D gap decomposition splits/ LVIS eval split + ModelNet40 test split logs/ Raw training logs (.log) for all 11 train runs README.md This file CLAIM_TO_FILE_MAP.md Per-paper-claim file mapping REPRODUCE.md Runbook for reproducing every numeric claim CITATIONS.md External data dependencies (Cap3D, etc.) LICENSE CC-BY-4.0 ``` ## What's NOT here - **Run-A T1, Run-A T2-MLP, Run-B v2 T1, Run-C T1, Run-0 T1 checkpoints** — these training runs were on temporary pod scratch storage that was reclaimed before snapshot. Per-step metrics, training logs, masks, and configs ARE in this release for all of them, so the runs are fully reproducible from the bundled code (see `REPRODUCE.md`). - **OpenShape training corpus point clouds** — not redistributed (~700 GB); download from the original OpenShape release per `CITATIONS.md`. - **Cap3D caption corpora** — required for the own-caption proxy (Table 1); download per `CITATIONS.md`. Reproducible aggregates for the captioner-sweep rows are bundled at `data/audit/G6_captioner_sweep_own.md` (in the supplementary ZIP, not this HF repo). ## How to load ```python from huggingface_hub import snapshot_download import torch, numpy as np, json # Data repo data_path = snapshot_download( repo_id="/openshape-contamination-axes-data", repo_type="dataset", ) mask = np.load(f"{data_path}/masks/run_C.npy") config = open(f"{data_path}/configs/run_c_v3_t2_mlp_config.yaml").read() # Model repo ckpt_path = snapshot_download( repo_id="/openshape-contamination-axes-checkpoints", ) state = torch.load(f"{ckpt_path}/run_c_v3_t2_mlp_best_lvis.pt", map_location="cpu") # State dict keys follow the standard OpenShape PointBERT layout ``` ## License - **Code, masks, configs, metrics, logs, predictions, splits:** CC-BY-4.0. - **Checkpoints:** released under the same license as the upstream OpenShape weights (MIT — see ). ## Citation To be added after the double-blind review period.