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arxiv: 2512.02482 · v2 · pith:M7465QCInew · submitted 2025-12-02 · 💻 cs.CV

G-SHARP: Gaussian Surgical Hardware Accelerated Real-time Pipeline

Vishwesh Nath , Javier G. Tejero , Aravind S. Kumar , Ruilong Li , Filippo Filicori , Mahdi Azizian , Sean D. Huver This is my paper

Pith reviewed 2026-05-17 02:56 UTC · model grok-4.3

classification 💻 cs.CV
keywords Gaussian splattingsurgical scene reconstructionreal-time 3D modelingdeformable tissueendoscopic reconstructionocclusion handlingHoloscan deployment
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The pith

G-SHARP is the first surgical reconstruction pipeline built natively on the open GSplat Gaussian rasterizer to support real-time modeling of deformable tissue.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to demonstrate that a complete surgical scene reconstruction system can reach high reconstruction quality and real-time speeds by starting from the commercially licensed GSplat library rather than from modified or non-commercial derivatives. A reader would care because current Gaussian-splatting methods for endoscopy often cannot move from research prototypes into actual operating rooms without legal or performance barriers. By adding explicit deformation modeling and occlusion handling on top of the native rasterizer, the framework produces competitive results on the EndoNeRF benchmark while running on standard NVIDIA edge hardware through a provided Holoscan SDK application. This combination directly targets the practical constraints of minimally invasive procedures where tissue moves and occlusions are common.

Core claim

G-SHARP is the first surgical pipeline built natively on the GSplat Apache-2.0 differentiable Gaussian rasterizer. This integration enables principled deformation modeling and robust occlusion handling, which together produce high-fidelity reconstructions on the EndoNeRF pulling benchmark together with speed-accuracy trade-offs suitable for intra-operative use. The authors supply a Holoscan SDK application that deploys the pipeline on NVIDIA IGX Orin and Thor edge hardware for real-time visualization in practical operating-room settings.

What carries the argument

Native integration with the GSplat differentiable Gaussian rasterizer augmented by dedicated deformation modeling and occlusion handling modules.

If this is right

  • Supports real-time 3D modeling of deformable tissue during minimally invasive procedures.
  • Achieves state-of-the-art reconstruction quality on the EndoNeRF pulling benchmark.
  • Maintains favorable speed-accuracy trade-offs without relying on non-commercial code changes.
  • Enables direct deployment on commercial NVIDIA edge hardware through the Holoscan SDK for operating-room visualization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the native-library approach proves reliable, similar direct integrations could reduce friction when moving other Gaussian-splatting techniques into regulated medical environments.
  • The same deformation and occlusion modules might transfer to related real-time tasks such as robotic instrument tracking or ultrasound volume reconstruction.
  • Wider availability of such edge-deployable pipelines could encourage development of closed-loop systems that update 3D models continuously during live surgery.

Load-bearing premise

That the native GSplat integration plus the added deformation and occlusion modules will deliver both the reported reconstruction quality and real-time frame rates on actual surgical hardware without requiring library modifications or per-case tuning.

What would settle it

A benchmark run of G-SHARP on the EndoNeRF pulling dataset using the stated NVIDIA IGX hardware that shows either lower reconstruction quality than existing methods or sustained frame rates below real-time thresholds.

read the original abstract

We propose G-SHARP, a commercially compatible, real-time surgical scene reconstruction framework designed for minimally invasive procedures that require fast and accurate 3D modeling of deformable tissue. While recent Gaussian splatting approaches have advanced real-time endoscopic reconstruction, existing implementations often depend on non-commercial derivatives, limiting deployability. G-SHARP overcomes these constraints by being the first surgical pipeline built natively on the GSplat (Apache-2.0) differentiable Gaussian rasterizer, enabling principled deformation modeling, robust occlusion handling, and high-fidelity reconstructions on the EndoNeRF pulling benchmark. Our results demonstrate state-of-the-art reconstruction quality with strong speed-accuracy trade-offs suitable for intra-operative use. Finally, we provide a Holoscan SDK application that deploys G-SHARP on NVIDIA IGX Orin and Thor edge hardware, enabling real-time surgical visualization in practical operating-room settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces G-SHARP, a commercially compatible real-time surgical scene reconstruction framework for minimally invasive procedures. It claims to be the first pipeline built natively on the Apache-2.0 GSplat differentiable Gaussian rasterizer, adding principled deformation modeling and robust occlusion handling to deliver high-fidelity reconstructions on the EndoNeRF pulling benchmark with strong speed-accuracy trade-offs, and provides a Holoscan SDK application for deployment on NVIDIA IGX Orin and Thor edge hardware.

Significance. If the performance claims hold, the work would be significant for enabling practical deployment of Gaussian splatting in operating rooms by providing a fully commercial-compatible implementation that supports real-time performance on edge hardware without reliance on non-commercial derivatives.

major comments (2)
  1. Abstract: the assertion of state-of-the-art reconstruction quality and strong speed-accuracy trade-offs on EndoNeRF supplies no quantitative metrics, ablation studies, or error bars, making it impossible to verify whether the central performance claims are supported by the data.
  2. Deployment description: no latency breakdowns, ablation on module overhead, or evidence that the added deformation and occlusion modules deliver real-time frame rates on IGX Orin/Thor without non-commercial patches or per-case tuning are provided; this is load-bearing for the intra-operative real-time claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating where revisions have been made to strengthen the presentation of our results and deployment claims.

read point-by-point responses

  1. Referee: Abstract: the assertion of state-of-the-art reconstruction quality and strong speed-accuracy trade-offs on EndoNeRF supplies no quantitative metrics, ablation studies, or error bars, making it impossible to verify whether the central performance claims are supported by the data.

    Authors: We agree that the abstract would be strengthened by including key quantitative support for the claims. In the revised version, we have added specific metrics from our EndoNeRF experiments, including PSNR, SSIM, and LPIPS values with comparisons to baselines, along with reported frame rates and error bars derived from multiple runs. The full ablation studies and detailed quantitative results remain in Section 4 and the supplementary material, but the abstract now provides verifiable numbers to substantiate the state-of-the-art and speed-accuracy statements. revision: yes

  2. Referee: Deployment description: no latency breakdowns, ablation on module overhead, or evidence that the added deformation and occlusion modules deliver real-time frame rates on IGX Orin/Thor without non-commercial patches or per-case tuning are provided; this is load-bearing for the intra-operative real-time claim.

    Authors: We partially agree on the need for greater transparency in the deployment section. The original manuscript reports overall real-time frame rates achieved on NVIDIA IGX Orin and Thor hardware via the Holoscan SDK application. In revision, we have added a latency breakdown table for the core pipeline components and an ablation study quantifying the overhead of the deformation modeling and occlusion handling modules. We confirm that G-SHARP uses the unmodified Apache-2.0 GSplat rasterizer with no non-commercial patches, and the reported performance was obtained without per-case tuning across the evaluated sequences. Additional profiling details have been moved to the supplementary material to keep the main text focused. revision: partial

Circularity Check

0 steps flagged

No circularity: engineering pipeline with external dependencies and no self-referential derivations

full rationale

The manuscript describes an integration of the external Apache-2.0 GSplat rasterizer with added deformation and occlusion modules, followed by deployment on Holoscan SDK for specific NVIDIA hardware. No equations, fitted parameters renamed as predictions, uniqueness theorems, or self-citation chains appear in the provided abstract or framing. The central claims rest on benchmark results (EndoNeRF) and engineering choices rather than any derivation that reduces to its own inputs by construction. This is a standard non-circular engineering contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the framework implicitly relies on standard assumptions of Gaussian splatting (e.g., scene representation as anisotropic Gaussians) and the correctness of the GSplat library, none of which are derived in the paper.

pith-pipeline@v0.9.0 · 5475 in / 1266 out tokens · 46967 ms · 2026-05-17T02:56:48.193004+00:00 · methodology

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Reference graph

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