Pith Number

pith:TZPOHLKR

pith:2026:TZPOHLKRQSDPBWERRRWVDOKWYV

not attested not anchored not stored refs resolved

Eff-WRFGS: Efficient Wireless Radiance Field Using 3D Gaussian Splatting

Chenghong Bian, Deniz Gunduz, Meng Hua

Eff-WRFGS adds learnable masks to 3D Gaussian primitives to prune wireless radiance fields for compact CSI prediction.

arxiv:2605.15324 v1 · 2026-05-14 · eess.SP

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{TZPOHLKRQSDPBWERRRWVDOKWYV}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Numerical results on the NeRF² dataset demonstrate that Eff-WRFGS achieves up to 44× storage reduction and 7× rendering speed-up with only marginal quality degradation.

C2weakest assumption

The learnable mask per Gaussian primitive can be trained to reliably identify and prune elements without systematically degrading CSI prediction accuracy for new transmitter locations (described in the abstract as guiding pruning while preserving rendering quality).

C3one line summary

Eff-WRFGS prunes 3D Gaussian primitives via learnable masks to deliver up to 44x storage reduction and 7x faster rendering for wireless radiance fields on the NeRF² dataset with marginal quality loss.

References

13 extracted · 13 resolved · 1 Pith anchors

[1] Channel estimation techniques based on pilot arrangement in OFDM systems, 2002

[2] Deep learning for massive MIMO CSI feedback, 2018

[3] RadioUNet: Fast radio map estimation with convolutional neural networks, 2021

[4] Learning radio environments by differentiable ray tracing, 2024

[5] A tutorial on environment- aware communications via channel knowledge map for 6g, 2024

Receipt and verification

First computed	2026-05-20T00:00:52.684118Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9e5ee3ad518486f0d8918c6d51b956c54103b965b796b0b125048c8c4926ec04

Aliases

arxiv: 2605.15324 · arxiv_version: 2605.15324v1 · doi: 10.48550/arxiv.2605.15324 · pith_short_12: TZPOHLKRQSDP · pith_short_16: TZPOHLKRQSDPBWER · pith_short_8: TZPOHLKR

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TZPOHLKRQSDPBWERRRWVDOKWYV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9e5ee3ad518486f0d8918c6d51b956c54103b965b796b0b125048c8c4926ec04

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3f6b7d5dc57a25ad68f881ff8096831ad1216d8eca9e7e8fbdefe74e4e6990c8",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-14T18:38:29Z",
    "title_canon_sha256": "e4d2f70d6ebb6daadd8bbef8ffb08a7a185c36af2ece9c6b7ac6149d7fc86263"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15324",
    "kind": "arxiv",
    "version": 1
  }
}