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arxiv: 2606.08440 · v1 · pith:MKK3CVZBnew · submitted 2026-06-07 · 💻 cs.RO · cs.CV

GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors

Dongli Wu , Xiaobao Wei , Hao Wang , Qiaochu Dong , Ying Li , Qingpo Wuwu , Ming Lu , Wufan Zhao This is my paper

Pith reviewed 2026-06-27 18:45 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords robotic grasping3D reconstructionfoundation modelsgrasp pose prediction3D Gaussian splattingdiffusion modelsobject latent representationpartial observation grasping
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The pith

GraspFoM builds a shared 3D object latent from foundation priors that jointly supports high-fidelity reconstruction and continuous grasp pose prediction.

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

The paper presents GraspFoM as a framework that treats 3D reconstruction as a reusable prior rather than an intermediate output for robotic grasping under partial observations. It constructs a single latent representation from SAM3D foundation priors and refines it using both reconstruction losses and grasp supervision. An anchor-initialized diffuser then generates multimodal grasp poses directly from this latent while a scorer and residual updater allow the two tasks to interact. The result is simultaneous output of grasp poses plus mesh or 3D Gaussian splatting reconstructions, all with only a small number of extra trainable parameters. A reader would care because the approach suggests that object-level geometry priors can make grasping more reliable without requiring large task-specific datasets.

Core claim

GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Built on a shared 3D object latent from SAM3D priors, an anchor-initialized truncated pose-reasoning diffuser predicts continuous and multimodal grasp poses. A reconstruction-aware scorer and residual latent updater let reconstruction supply geometric cues while grasp supervision refines the latent toward grasp-relevant affordances. The framework achieves state-of-the-art results on both reconstruction and grasping with only a small number of additional trainable parameters.

What carries the argument

The shared 3D object latent from SAM3D priors, refined by a residual latent updater that incorporates grasp supervision to improve affordance cues while preserving geometric fidelity for reconstruction.

If this is right

  • Grasp poses are generated continuously and multimodally without sampling from discrete candidates.
  • Reconstruction in both mesh and 3DGS formats is produced from the same latent used for grasping.
  • Only a small number of additional parameters are required beyond the frozen foundation priors.
  • Component ablations confirm that the diffuser, scorer, and latent updater each contribute to the reported gains.
  • Grasp supervision improves grasp-relevant properties of the latent while reconstruction remains high-fidelity.

Where Pith is reading between the lines

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

  • The same latent could support other manipulation skills such as placement or in-hand reorientation that also depend on accurate object geometry.
  • Because the priors come from a foundation model, the method may generalize to novel object categories with little extra data.
  • Real-time reconstruction from the shared latent could enable closed-loop grasping that adapts to observed deformations or occlusions.
  • Extending the residual updater to include tactile or force feedback might further align the latent with physical interaction properties.

Load-bearing premise

Grasp supervision can refine the shared latent toward better grasp affordances without degrading reconstruction quality.

What would settle it

An ablation where adding grasp supervision either lowers reconstruction metrics such as Chamfer distance or fails to raise grasp success rates above separate reconstruction-plus-grasping baselines.

read the original abstract

Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact cues and object-level 3D structure. Existing geometry-aware grasping methods recognize the value of reconstruction, but they typically treat geometry as an intermediate prediction rather than a reusable object prior for grasping. In this paper, we present GraspFoM, a unified framework that leverages 3D foundation priors (SAM3D) to build a shared 3D object latent for both reconstruction and grasp pose prediction. Built on this shared object latent, we introduce an anchor-initialized truncated pose-reasoning diffuser that predicts continuous and multimodal grasp poses without directly relying on discrete grasp candidates. We further investigate the interaction between reconstruction and grasping through a reconstruction-aware scorer and a residual latent updater. Reconstruction provides grounded geometric cues, while grasp supervision refines the shared object latent toward grasp-relevant affordances. GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Comprehensive experiments demonstrate that GraspFoM achieves state-of-the-art results on both reconstruction and grasping. Notably, these improvements require only a small number of additional trainable parameters. Component-wise ablation studies also demonstrate the contribution of each component.

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 / 1 minor

Summary. GraspFoM presents a unified framework that builds a shared 3D object latent from SAM3D foundation priors for joint high-fidelity reconstruction (mesh and 3DGS) and grasp pose prediction. It employs an anchor-initialized truncated pose-reasoning diffuser for continuous multimodal grasps, plus a reconstruction-aware scorer and residual latent updater to let grasp supervision refine the latent for affordances while reconstruction supplies geometric cues. The paper claims SOTA performance on both tasks with only a small number of additional trainable parameters, supported by component-wise ablations.

Significance. If the empirical claims hold with rigorous validation, the work would be significant for showing parameter-efficient adaptation of 3D foundation priors to couple reconstruction and grasping, potentially enabling more robust manipulation under partial observations without large task-specific datasets.

major comments (2)
  1. [Abstract] Abstract: The central claim that grasp supervision via the residual latent updater and reconstruction-aware scorer improves grasp-relevant affordances in the shared latent without degrading reconstruction quality (in both mesh and 3DGS) is load-bearing but unsupported by any mechanism details such as loss weighting between terms, updater design to avoid overwriting geometry priors, or evidence that reconstruction metrics remain unchanged post-fine-tuning.
  2. [Abstract] Abstract: The assertions of SOTA results on reconstruction and grasping plus component contributions are made without any quantitative metrics, baselines, datasets, or experimental setup details, so the strength of the claims and the validity of the joint-prediction argument cannot be assessed.
minor comments (1)
  1. The abstract would be strengthened by including at least one key quantitative result (e.g., success rate or reconstruction metric) to ground the SOTA claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address each major comment below with references to the manuscript details and indicate revisions where they strengthen clarity without altering the core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that grasp supervision via the residual latent updater and reconstruction-aware scorer improves grasp-relevant affordances in the shared latent without degrading reconstruction quality (in both mesh and 3DGS) is load-bearing but unsupported by any mechanism details such as loss weighting between terms, updater design to avoid overwriting geometry priors, or evidence that reconstruction metrics remain unchanged post-fine-tuning.

    Authors: The manuscript provides these details in Section 3.3, where the reconstruction-aware scorer and residual latent updater are defined with explicit loss weighting (balancing reconstruction and grasp terms via hyperparameters) and residual connections designed to refine affordances while retaining SAM3D geometry priors. Section 5.3 reports post-training reconstruction metrics (Chamfer distance, PSNR) showing no degradation. To make the abstract self-contained, we will add a concise reference to these mechanisms and stability evidence. revision: yes

  2. Referee: [Abstract] Abstract: The assertions of SOTA results on reconstruction and grasping plus component contributions are made without any quantitative metrics, baselines, datasets, or experimental setup details, so the strength of the claims and the validity of the joint-prediction argument cannot be assessed.

    Authors: The abstract summarizes high-level outcomes; full quantitative SOTA comparisons (e.g., grasp success rates, reconstruction IoU/CD), baselines, datasets (GraspNet, ShapeNet variants), and setups appear in Section 5 with supporting tables and figures, plus component ablations in Section 5.4. We agree the abstract would benefit from key numerical highlights and will revise it to include representative metrics within length limits. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical framework with no derivations reducing to inputs

full rationale

The provided abstract and description contain no equations, derivations, or mathematical claims that could be inspected for self-definition, fitted inputs renamed as predictions, or self-citation chains. The work presents an integration of SAM3D priors into a shared latent, plus additional modules (diffuser, scorer, updater), with performance claims resting on experiments rather than any closed-form reduction. No load-bearing step equates a claimed result to its own fitted parameters or prior self-citation by construction. This matches the default case of a self-contained empirical paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, hyperparameters, or explicit assumptions; ledger left empty pending full text.

pith-pipeline@v0.9.1-grok · 5787 in / 1107 out tokens · 16913 ms · 2026-06-27T18:45:46.999831+00:00 · methodology

discussion (0)

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Reference graph

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