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arxiv: 2604.18790 · v1 · submitted 2026-04-20 · 💻 cs.CV

EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion

Johny J. Lopez , Md Meftahul Ferdaus , Mahdi Abdelguerfi , Anton Netchaev , Steven Sloan , Ken Pathak , Kendall N. Niles This is my paper

Pith reviewed 2026-05-10 05:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords depth completionsparse LiDARreal-time inferencemulti-modal fusionConvNeXtCSPNKITTIedge AI
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The pith

EfficientPENet shows that a ConvNeXt-based two-branch network with late fusion and CSPN can complete sparse LiDAR depth maps in real time on edge hardware while preserving competitive accuracy.

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

The paper introduces EfficientPENet to address the gap between high-accuracy depth completion methods and the need for real-time performance on resource-limited devices. It builds a network with separate RGB and depth branches that use a modern ConvNeXt backbone, sparsity-invariant convolutions in the depth path, late feature fusion, and a CSPN refinement stage. A position-aware test-time augmentation further improves results without extra training cost. If the design works as described, it would allow robots and autonomous systems to generate dense depth from affordable sparse sensors at video rates on embedded chips. This matters because accurate 3D perception is a bottleneck for many practical applications that cannot afford heavy GPUs.

Core claim

EfficientPENet is a two-branch depth completion network that replaces conventional ResNet encoders with ConvNeXt blocks. The RGB branch uses ImageNet-pretrained ConvNeXt with LayerNorm and stochastic depth, while the depth branch employs sparsity-invariant convolutions. Late fusion merges the features, followed by multi-scale decoding and CSPN refinement. Position-aware test-time augmentation corrects coordinates during flipping. On KITTI, this yields an RMSE of 631.94 mm using 36.24 million parameters at 20.51 ms latency, or 48.76 FPS, which is 3.7 times fewer parameters and 23 times faster than BP-Net with similar accuracy.

What carries the argument

The lightweight multi-modal fusion architecture combining ConvNeXt RGB encoding, sparsity-invariant depth encoding, late fusion, and CSPN propagation for efficient dense depth prediction from sparse inputs.

If this is right

  • Real-time depth completion becomes feasible on NVIDIA Jetson-class hardware.
  • Model size shrinks enough for deployment where memory is limited.
  • Accuracy remains close enough to heavier models for practical robotic use.
  • Test-time augmentation provides free accuracy gains through coordinate correction.

Where Pith is reading between the lines

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

  • The approach may generalize to other sensor fusion tasks where one modality is sparse.
  • Modern ConvNeXt backbones could replace older encoders in similar vision tasks without major redesign.
  • Further speedups might come from model quantization given the already low latency.

Load-bearing premise

That the combination of ConvNeXt, sparsity-invariant convolutions, late fusion, CSPN, and position-aware augmentation delivers the reported accuracy-speed tradeoff without overfitting to the KITTI benchmark specifics.

What would settle it

Running the model on a held-out outdoor dataset with different LiDAR sparsity patterns and measuring whether RMSE stays below 700 mm at similar frame rates would confirm or refute the generalization of the efficiency gains.

Figures

Figures reproduced from arXiv: 2604.18790 by Anton Netchaev, Johny J. Lopez, Kendall N. Niles, Ken Pathak, Mahdi Abdelguerfi, Md Meftahul Ferdaus, Steven Sloan.

Figure 1
Figure 1. Figure 1: Overview of the EfficientPENet architecture. The RGB branch (left) uses a pretrained ConvNeXt encoder; the depth [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Model complexity comparison. EfficientPENet reduces [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Normalized multi-metric comparison. Each axis is [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison between PENet and EfficientPENet on five KITTI validation scenes. From left to right: RGB [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study showing incremental RMSE reduction [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
read the original abstract

Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing methods achieve high accuracy on standard benchmarks but rely on heavy backbone architectures that preclude real-time deployment on embedded hardware. We present EfficientPENet, a two-branch depth completion network that replaces the conventional ResNet encoder with a modernized ConvNeXt backbone, introduces sparsity-invariant convolutions for the depth stream, and refines predictions through a Convolutional Spatial Propagation Network (CSPN). The RGB branch leverages ImageNet-pretrained ConvNeXt blocks with Layer Normalization, 7x7 depthwise convolutions, and stochastic depth regularization. Features from both branches are merged via late fusion and decoded through a multi-scale deep supervision strategy. We further introduce a position-aware test-time augmentation scheme that corrects coordinate tensors during horizontal flipping, yielding consistent error reduction at inference. On the KITTI depth completion benchmark, EfficientPENet achieves an RMSE of 631.94 mm with 36.24M parameters and a latency of 20.51 ms, operating at 48.76 FPS. This represents a 3.7 times reduction in parameters and a 23 times speedup relative to BP-Net, while maintaining competitive accuracy. These results establish EfficientPENet as a practical solution for real-time depth completion on resource-constrained edge platforms such as the NVIDIA Jetson.

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. The paper presents EfficientPENet, a two-branch depth completion network for sparse LiDAR and RGB inputs. It modernizes the encoder with a ConvNeXt backbone, adds sparsity-invariant convolutions to the depth branch, performs late fusion, applies CSPN refinement, and introduces position-aware test-time augmentation. On the KITTI depth completion benchmark the method reports an RMSE of 631.94 mm, 36.24 M parameters, 20.51 ms latency and 48.76 FPS, claiming a 3.7× parameter reduction and 23× speedup relative to BP-Net while remaining competitive in accuracy.

Significance. If the efficiency numbers are obtained under identical hardware, resolution and optimization conditions as the cited baseline, the work would be significant for practical real-time 3D perception on embedded platforms such as the NVIDIA Jetson. The architectural choices (ConvNeXt + sparsity handling + CSPN) target a known deployment bottleneck; reproducible verification of the claimed speed/accuracy trade-off would strengthen the case for edge robotics applications.

major comments (2)
  1. [Abstract] Abstract: the central efficiency claim states a 3.7× parameter reduction and 23× speedup versus BP-Net, yet supplies no indication that BP-Net was re-implemented, compiled, and timed on the identical NVIDIA Jetson platform, input resolution, batch size and optimization settings used for EfficientPENet. Because these ratios are load-bearing for the “real-time on edge” positioning, the comparison must be substantiated with explicit re-measurement details.
  2. [Abstract] Abstract / Experiments section: the manuscript reports concrete benchmark numbers (RMSE 631.94 mm, 36.24 M parameters, 48.76 FPS) but provides no training protocol, hyper-parameter schedule, validation split, error bars, or ablation studies. Without these elements it is impossible to determine whether the reported accuracy arises from the proposed combination of ConvNeXt, sparsity-invariant convolutions, late fusion, CSPN and position-aware TTA or from post-hoc benchmark tuning.
minor comments (1)
  1. [Abstract] The abstract would benefit from a brief statement of the exact KITTI depth-completion split and evaluation protocol used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important issues of reproducibility and fair comparison. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central efficiency claim states a 3.7× parameter reduction and 23× speedup versus BP-Net, yet supplies no indication that BP-Net was re-implemented, compiled, and timed on the identical NVIDIA Jetson platform, input resolution, batch size and optimization settings used for EfficientPENet. Because these ratios are load-bearing for the “real-time on edge” positioning, the comparison must be substantiated with explicit re-measurement details.

    Authors: We agree that the efficiency claims require explicit substantiation. The parameter reduction (3.7×) is computed from the parameter count reported in the original BP-Net publication. The speedup (23×) is based on our measured latency for EfficientPENet (20.51 ms / 48.76 FPS on NVIDIA Jetson) compared against the latency figures published for BP-Net. In the revision we will add a dedicated implementation and evaluation subsection that specifies the exact hardware (Jetson model), software environment, input resolution, batch size (1 for inference), and measurement methodology. We will also state whether BP-Net was re-timed under identical conditions or clarify that the comparison uses published numbers, thereby removing any ambiguity. revision: yes

  2. Referee: [Abstract] Abstract / Experiments section: the manuscript reports concrete benchmark numbers (RMSE 631.94 mm, 36.24 M parameters, 48.76 FPS) but provides no training protocol, hyper-parameter schedule, validation split, error bars, or ablation studies. Without these elements it is impossible to determine whether the reported accuracy arises from the proposed combination of ConvNeXt, sparsity-invariant convolutions, late fusion, CSPN and position-aware TTA or from post-hoc benchmark tuning.

    Authors: We acknowledge that the current manuscript omits these essential experimental details. The revised version will contain an expanded Experiments section that reports: the full training protocol and hyper-parameter schedule (optimizer, learning-rate schedule, batch size, number of epochs, loss weighting); the precise KITTI validation split employed; error bars obtained from multiple independent training runs where feasible; and a set of ablation studies that isolate the contribution of each proposed element (ConvNeXt backbone, sparsity-invariant convolutions, late fusion, CSPN refinement, and position-aware TTA). These additions will allow readers to verify that the reported accuracy results from the architectural choices rather than post-hoc tuning. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results on external KITTI benchmark with no self-referential derivations

full rationale

The paper describes an architecture (ConvNeXt backbone, sparsity-invariant convolutions, late fusion, CSPN refinement, position-aware TTA) and reports measured performance (RMSE 631.94 mm, 36.24M params, 48.76 FPS) on the public KITTI depth completion benchmark. No equations, fitted parameters, or first-principles derivations are presented that reduce to the inputs by construction. Comparisons to BP-Net are external benchmarks; even if re-implementation details are incomplete, this does not create internal circularity. The derivation chain is self-contained as standard empirical ML reporting against an independent dataset.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no mathematical derivations, free parameters, or new postulated entities are described. All components are standard neural-network building blocks drawn from prior literature.

pith-pipeline@v0.9.0 · 5580 in / 1303 out tokens · 48476 ms · 2026-05-10T05:17:53.379454+00:00 · methodology

discussion (0)

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