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arxiv: 2605.26456 · v1 · pith:SPCZ6GPMnew · submitted 2026-05-26 · 💻 cs.CV

Sparse-LiDAR Prompting of Monocular Geometry Foundations: An Empirical Study Toward Long-Range Driving Depth

Kai Zheng , Qiang Feng , Xingjian Liu , Wenquan Tan , Yuan Li This is my paper

Pith reviewed 2026-06-29 18:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords monocular depth estimationsparse LiDARlong-range depthpoint-map foundation modelsdensity-agnostic trainingpartial convolutionautonomous drivingVirtual KITTI CARLA
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The pith

Sparse LiDAR injection into MoGe-2 cuts absolute relative depth error by 39-51% at 100-150 meters on simulated driving scenes

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

The paper introduces SLIM as the first adaptation of the MoGe-2 point-map foundation model to accept truly sparse LiDAR inputs rather than pre-interpolated dense priors. It combines a partial-convolution sparse encoder with a multi-scale fusion neck that merges LiDAR features into the decoder at five scales, trained density-agnostically by sampling injection ratios randomly from 0.005 to 0.30. On Virtual KITTI and CARLA, this produces 39-51% lower absolute relative error than the unmodified MoGe-2 baseline specifically in the 100-150 meter range, with ablations confirming partial-convolution benefits across most density settings. The work targets the absence of systematic long-range stratified evaluation in prior sparse-prompting approaches focused on indoor or short-range data.

Core claim

SLIM adapts MoGe-2 to accept truly sparse LiDAR input through a partial-convolution sparse encoder and a multi-scale fusion neck that fuses LiDAR features into the point-map decoder at five scales. The model is trained density-agnostically with random injection ratios in [0.005, 0.30] so a single set of weights handles diverse input densities. On Virtual KITTI and CARLA this yields an absolute relative error reduction of approximately 39-51% relative to the MoGe-2 baseline at 100-150 m distances.

What carries the argument

Partial-convolution sparse encoder plus multi-scale fusion neck that injects LiDAR features into the point-map decoder at five scales, under density-agnostic training with random injection ratios

If this is right

  • A single model trained density-agnostically performs across injection ratios from 0.5% to 30% without retraining
  • Partial-convolution injection improves absolute relative error and RMSE on Virtual KITTI in all six tested ratios and improves absolute relative error in five of six ratios on CARLA
  • Error reductions concentrate in the long-range regime (100-150 m) where baseline monocular point-map models show largest shortfalls
  • The method operates directly on point-map foundations without requiring pre-interpolated dense priors used in earlier disparity-based prompting work

Where Pith is reading between the lines

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

  • If the simulated gains hold, sparse-LiDAR prompting could lower the sensor density required for reliable long-range perception in driving systems
  • The five-scale fusion pattern may transfer to other point-map foundation models beyond MoGe-2 with similar architecture
  • Real sensor noise and calibration drift absent from simulation could narrow the observed accuracy margin and would require targeted robustness tests
  • The density-agnostic training schedule might also stabilize performance when LiDAR point density varies dynamically during a drive

Load-bearing premise

Performance gains measured on the simulated Virtual KITTI and CARLA environments will translate to real-world driving data with actual sensors, lighting, and weather variations

What would settle it

Evaluating SLIM and the MoGe-2 baseline on real-world long-range depth datasets such as the original KITTI or nuScenes with ground-truth depths beyond 80 m would show whether the reported error reductions persist outside simulation

Figures

Figures reproduced from arXiv: 2605.26456 by Kai Zheng, Qiang Feng, Wenquan Tan, Xingjian Liu, Yuan Li.

Figure 1
Figure 1. Figure 1: SLIM architecture. The visual branch is the MoGe-2 [1] backbone (DINOv2 ViT-S/14; intermediate features taken from layers 5 and 11; projected feature dimension 384), producing a five-level visual feature pyramid (channels 384, 256, 128, 64, 32). The sparse-geometry branch applies local sparse￾depth filling (nearest-neighbor propagation) followed by a five-stage PartialConv encoder (strides [1, 2, 2, 2, 2];… view at source ↗
Figure 2
Figure 2. Figure 2: AbsRel as a function of distance bin. SLIM’s curve remains comparatively flat across distance, whereas the MoGe-2 baseline’s error grows steeply in the long-range regime. Drawn from [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison on two Virtual KITTI scenes. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Sparse-LiDAR-prompted depth foundation models (PromptDA, Prior Depth Anything, DMD3C) have shown strong results on indoor scenes or within KITTI's standard 80-meter evaluation cap. However, two limitations remain: (i) systematic distance-stratified evaluation in long-range driving regimes (50-150 m) is largely absent; (ii) prior approaches built on disparity-based foundations rely on pre-interpolated dense priors, leaving truly sparse LiDAR injection on point-map foundations (e.g., MoGe-2, NeurIPS 2025) unexplored. We present SLIM (Sparse-LiDAR Injected Monocular geometry), the first adaptation of MoGe-2 to accept truly sparse LiDAR input. SLIM integrates a partial-convolution sparse encoder with a multi-scale fusion neck that fuses LiDAR features into the point-map decoder at five scales. We adopt density-agnostic training (random injection ratio in [0.005, 0.30]) so a single model serves diverse input densities. On Virtual KITTI and CARLA, SLIM reduces the absolute relative error of the MoGe-2 baseline by approximately 39-51% at 100-150 m. Ablation across six injection ratios shows partial-convolution injection improves both AbsRel and RMSE on Virtual KITTI in all six settings; on CARLA, AbsRel improves in five of six settings (one near-tie at 0.015 differs by 0.0013), and RMSE is comparable across encoders, with partial-convolution improving in three settings (by up to 0.31 unit) and losing by at most 0.11 unit in the other three.

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 / 2 minor

Summary. The manuscript introduces SLIM, an adaptation of the MoGe-2 point-map foundation model to accept truly sparse LiDAR inputs via a partial-convolution sparse encoder and multi-scale fusion neck at five scales. It uses density-agnostic training with random injection ratios in [0.005, 0.30] and reports that SLIM reduces AbsRel of the MoGe-2 baseline by 39-51% at 100-150 m on Virtual KITTI and CARLA, with ablations across six injection ratios showing partial-convolution improvements in AbsRel on both datasets in nearly all settings.

Significance. If the reported numerical gains hold under the described protocol, the work supplies a useful empirical ablation on sparse prompting of point-map foundations in long-range regimes beyond the typical 80 m KITTI cap, with the density-agnostic training and consistent cross-ratio gains as clear strengths. The partial-convolution design is shown to be effective in the provided synthetic settings.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (results): the central 39-51% AbsRel reduction claim at 100-150 m is presented without error bars, standard deviations across runs, or explicit description of the MoGe-2 baseline implementation details and any simulation-specific artifacts (e.g., perfect depth values or idealized sparsity) that could affect the long-range regime; these omissions directly impact assessment of result reliability.
  2. [§1 and §5] §1 and §5 (discussion): the framing as an empirical study 'toward long-range driving depth' relies on the assumption that gains on Virtual KITTI/CARLA will inform real driving, yet no analysis or caveats address domain shift factors such as sensor noise, calibration, or weather; while the synthetic results themselves can stand, this limits the load-bearing relevance of the application claim.
minor comments (2)
  1. [§3.2] Figure captions and §3.2: the multi-scale fusion neck diagram would benefit from explicit labeling of the five fusion scales and how partial-convolution features are injected at each.
  2. [Results tables] Table 1/2 (assumed results tables): ensure all six injection ratios are listed with both AbsRel and RMSE for both encoders to allow direct comparison of the near-tie case mentioned in the abstract.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and the recommendation for minor revision. We address each major comment below and will incorporate the agreed changes in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (results): the central 39-51% AbsRel reduction claim at 100-150 m is presented without error bars, standard deviations across runs, or explicit description of the MoGe-2 baseline implementation details and any simulation-specific artifacts (e.g., perfect depth values or idealized sparsity) that could affect the long-range regime; these omissions directly impact assessment of result reliability.

    Authors: We agree the omissions affect reliability assessment. All reported results used single runs due to computational limits, so error bars and standard deviations cannot be added; we will explicitly state this limitation in the revised §4. We will expand the MoGe-2 baseline description in §4 to detail the point-map prompting procedure and any simulator-specific assumptions. A new paragraph will discuss simulation artifacts such as perfect ground-truth depths and idealized sparsity patterns. revision: partial

  2. Referee: [§1 and §5] §1 and §5 (discussion): the framing as an empirical study 'toward long-range driving depth' relies on the assumption that gains on Virtual KITTI/CARLA will inform real driving, yet no analysis or caveats address domain shift factors such as sensor noise, calibration, or weather; while the synthetic results themselves can stand, this limits the load-bearing relevance of the application claim.

    Authors: We agree that explicit caveats are needed. In the revised §1 and §5 we will add a dedicated limitations paragraph addressing domain shift, specifically noting the lack of real sensor noise, calibration errors, and weather variation in the synthetic benchmarks. This will clarify that the reported gains are confined to controlled synthetic settings and that real-world transfer would require separate validation. revision: yes

Circularity Check

0 steps flagged

Empirical ablation study; no derivation chain present

full rationale

The paper reports experimental results from an ablation study on simulated datasets (Virtual KITTI, CARLA) using density-agnostic training and partial-convolution fusion. No equations, derivations, or first-principles claims are described that could reduce to fitted parameters or self-citations by construction. The central numerical claims (39-51% AbsRel reduction) are direct metric outputs from test-set evaluation, not outputs of any internal model that re-uses the same quantities as inputs. This is a standard empirical paper with no load-bearing mathematical steps to inspect for circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the existing MoGe-2 architecture being extensible via standard partial-convolution and fusion modules; the main empirical contribution is the integration and density-agnostic training rather than new theoretical primitives.

free parameters (1)
  • random injection ratio range [0.005, 0.30]
    Chosen to enable a single model to handle varying LiDAR densities during training.
axioms (1)
  • domain assumption MoGe-2 point-map decoder can accept multi-scale feature fusion from a partial-convolution sparse encoder without loss of its original geometric properties
    Invoked when describing the five-scale fusion neck integration.

pith-pipeline@v0.9.1-grok · 5853 in / 1294 out tokens · 43864 ms · 2026-06-29T18:50:09.749665+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

11 extracted references · 10 canonical work pages · 4 internal anchors

  1. [1]

    MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

    R. Wanget al.MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details. In NeurIPS, 2025. arXiv:2507.02546

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Linet al.Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation

    H. Linet al.Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation. In CVPR, 2025. arXiv:2412.14015. 5

    work page arXiv 2025

  3. [3]

    Depth anything with any prior,

    Depth Anything with Any Prior. arXiv:2505.10565

    work page arXiv

  4. [4]

    Lianget al.Distilling Monocular Foundation Model for Fine-grained Depth Completion (DMD3C)

    J. Lianget al.Distilling Monocular Foundation Model for Fine-grained Depth Completion (DMD3C). InCVPR, 2025. arXiv:2503.16970

    work page arXiv 2025

  5. [5]

    Yanget al.Depth Anything

    L. Yanget al.Depth Anything. InCVPR, 2024. arXiv:2401.10891

    work page arXiv 2024

  6. [6]

    arXiv:2412.14103

    TTA-Depth: Test-Time Adaptation for Rescaling Disparity in Zero-Shot Metric Depth Estimation. arXiv:2412.14103

    work page arXiv

  7. [7]

    arXiv:2502.02144

    DOC-Depth: Dense Depth Generation from Any LiDAR Sensor. arXiv:2502.02144

    work page arXiv

  8. [8]

    Image Inpainting for Irregular Holes Using Partial Convolutions

    G. Liuet al.Image Inpainting for Irregular Holes Using Partial Convolutions. InECCV, 2018. arXiv:1804.07723

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  9. [9]

    Squeeze-and-Excitation Networks

    J. Huet al.Squeeze-and-Excitation Networks. InCVPR, 2018. arXiv:1709.01507

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  10. [10]

    Virtual KITTI 2

    Y. Cabonet al.Virtual KITTI 2. arXiv:2001.10773

    work page internal anchor Pith review Pith/arXiv arXiv 2001

  11. [11]

    Dosovitskiyet al.CARLA: An Open Urban Driving Simulator

    A. Dosovitskiyet al.CARLA: An Open Urban Driving Simulator. InCoRL, 2017. 6

    2017