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Record W6963839344 · doi:10.2312/egs.20251050

Parallel Dense-Geometry-Format Topology Decompression

2025· article· en· W6963839344 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEurographics · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsShaderPolygon meshVertex (graph theory)Intersection (aeronautics)Triangle meshRepresentation (politics)Topology (electrical circuits)

Abstract

fetched live from OpenAlex

Dense Geometry Format (DGF) [BBM24] is a hardware-friendly representation for compressed triangle meshes specifically designed to support GPU hardware ray tracing. It decomposes a mesh into meshlets, i.e., small meshes with up to 64 positions, triangles, primitive indices, and opacity values, in a 128-byte block. However, accessing a triangle requires a slow sequential decompression algorithm with O(T) steps, where T is the number of triangles in a DGF block. We propose a novel parallel algorithm with O(logT) steps for arbitrary T. For DGF, where T ≤ 64, we transform our algorithm to allow O(1) access. We believe that our algorithm is suitable for hardware implementations. With our algorithm, a custom intersection shader outperforms the existing serial decompression method. Further, our mesh shader implementation achieves competitive rasterization performance with the vertex pipeline. Finally, we show how our method may parallelize other topology decompression schemes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.266
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it