arxiv: 2605.11799 · v1 · submitted 2026-05-12 · 💻 cs.CV

SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

Markus Essl , Marta Moscati , Mubashir Noman , Muhammad Zaigham Zaheer , Usman Naseem , Shah Nawaz , Markus Schedl This is my paper

Pith reviewed 2026-05-13 05:50 UTC · model grok-4.3

classification 💻 cs.CV

keywords sensor fusion3D object detectionrobustnesscamera LiDARBEV representationmultimodalsensor corruptionautonomous vehicles

0 comments p. Extension

The pith

A fusion module for camera and LiDAR data keeps 3D object detection accurate when one sensor fails or produces corrupted input.

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

The paper introduces a framework-agnostic fusion module that combines camera and LiDAR inputs for 3D object detection. The module is built to handle cases where one modality is missing or corrupted by noise, weather effects, or outright sensor failure. The authors plug the module into the existing BEVFusion system and evaluate it on the MultiCorrupt dataset. Results show consistent gains over prior unified BEV approaches across many deterioration types, with top performance in extreme weather and failure scenarios.

Core claim

The central claim is that inserting a dedicated fusion module for camera and LiDAR data allows the detector to retain accuracy under missing or corrupted modalities, substantially outperforming standard unified representation methods across a wide range of sensor deterioration scenarios and reaching state-of-the-art results specifically for corruptions caused by extreme weather conditions and sensor failure.

What carries the argument

The framework-agnostic fusion module that converts camera and LiDAR data into a bird's-eye view representation while explicitly managing missing or corrupted inputs.

If this is right

The module can be added to existing BEV fusion frameworks without major architectural changes.
Detection accuracy is maintained under a wide range of missing and corrupted modality scenarios.
State-of-the-art results are reached for cases of extreme weather and sensor failure.
The approach reduces vulnerability to sensor deterioration compared with standard unified BEV fusion.

Where Pith is reading between the lines

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

If the gains hold outside simulation, autonomous vehicles could operate more reliably when sensors degrade.
The results imply that explicit robustness handling is more effective than relying on unified representations alone.
The same module design could be tested on other multimodal tasks such as semantic segmentation or tracking.

Load-bearing premise

The performance gains measured on the simulated MultiCorrupt dataset will transfer to actual physical sensor malfunctions and corruptions.

What would settle it

A test in which a vehicle runs the module alongside standard BEVFusion while one real sensor is physically disabled or heavily corrupted by weather, then checks whether the claimed accuracy advantage disappears.

read the original abstract

Multimodal sensor fusion has demonstrated remarkable performance improvements over unimodal approaches in 3D object detection for autonomous vehicles. Typically, existing methods transform multimodal data from independent sensors, such as camera and LiDAR, into a unified bird's-eye view (BEV) representation for fusion. Although effective in ideal conditions, this strategy suffers from substantial performance deterioration when camera or LiDAR data are missing, corrupted, or noisy. To address this vulnerability, we develop a framework-agnostic fusion module for camera and LiDAR data that allows for handling cases when one of the two modalities is missing or corrupted. To demonstrate the effectiveness of our module, we instantiate it in BEVFusion [1], a well-established framework to combine camera and LiDAR data for 3D object detection. By means of quantitative experiments on the MultiCorrupt dataset, we demonstrate that our module achieves favorable performance improvements under scenarios of missing and corrupted modalities, substantially outperforming existing unified representation approaches across a wide range of sensor deterioration scenarios and reaching state-of-the-art performance in scenarios of corrupted modality due to extreme weather conditions and sensor failure.

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.

Desk Editor's Note private letter to a colleague

SB-BEVFusion adds a practical, framework-agnostic module to BEVFusion that improves 3D detection under missing or corrupted sensors on the MultiCorrupt benchmark.

read the letter

The main thing to know is that this paper introduces a fusion module for handling sensor dropout and corruption in BEV-based multimodal detection, and it delivers measurable gains when plugged into BEVFusion on the MultiCorrupt dataset. They keep the core BEVFusion pipeline mostly intact and add an adaptive component that deals with cases where camera or LiDAR data is missing or noisy. Experiments across a range of simulated deterioration scenarios, including extreme weather and outright sensor failure, show it beating prior unified-representation baselines and hitting SOTA numbers in several of the tougher conditions. That focus on real deployment pain points is the useful part. The module is presented as easy to drop into similar frameworks, which broadens its potential reach without requiring a full redesign. The evaluation uses an external benchmark with direct comparisons, so the numbers stand on their own rather than depending on author-defined parameters. What the paper does well is keep the contribution targeted and show the robustness improvements with tables that cover multiple corruption types. The claims line up with the reported results, and there are no obvious gaps in the methods or internal contradictions once you look at the full text. The soft spots are limited and expected for this kind of work. Performance still drops under heavy corruption, which is realistic, and the gains rest on simulated corruptions rather than live hardware failures. That assumption about simulation-to-real transfer is the main one left open, though the paper does not overclaim it as a complete fix. The advance is incremental rather than foundational, but the experiments are solid enough to support the narrower robustness claim. This paper is for people working on multimodal perception stacks for autonomous driving who already use or extend BEVFusion-style methods and need better handling of imperfect inputs. A reader focused on robustness or deployment issues would get concrete value from the module design and the benchmark numbers. It has enough grounding in methods, results, and comparisons to deserve a serious referee rather than a desk reject. I would recommend sending it to peer review.

Referee Report

0 major / 3 minor

Summary. The paper introduces SB-BEVFusion, a framework-agnostic fusion module for camera and LiDAR data in bird's-eye-view (BEV) representations for 3D object detection. The module is designed to maintain performance when one modality is missing or corrupted. It is instantiated within the BEVFusion framework and evaluated quantitatively on the MultiCorrupt dataset, where it is claimed to outperform prior unified-representation methods across various sensor deterioration scenarios and to achieve state-of-the-art results under extreme weather and sensor-failure corruptions.

Significance. If the reported gains on MultiCorrupt hold under the described experimental conditions, the work would be significant for autonomous-driving perception, as it directly targets robustness to realistic sensor malfunctions without requiring major architectural changes to existing BEV fusion pipelines. The framework-agnostic construction and explicit comparisons to prior unified methods constitute a clear strength.

minor comments (3)

The abstract and introduction claim 'state-of-the-art performance' under specific corruptions; the results section should explicitly list the exact baselines, number of runs, and statistical significance tests used to support this claim.
Section 3 (method) should include a clear diagram or pseudocode showing how the SB-BEVFusion module interfaces with the existing BEVFusion backbone when a modality is absent, to make the 'framework-agnostic' property immediately verifiable.
Table captions in the experimental results should state the exact corruption parameters (e.g., noise levels, missing ratios) used in MultiCorrupt to allow direct reproduction.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive evaluation of our work and the recommendation for minor revision. The provided summary accurately captures the core contribution of SB-BEVFusion as a framework-agnostic module that enhances robustness in BEV-based multimodal 3D object detection under sensor malfunction and corruption scenarios.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes a framework-agnostic fusion module instantiated inside BEVFusion and reports empirical gains on the external MultiCorrupt benchmark against prior unified-representation baselines. No equations, fitted parameters, or uniqueness claims reduce by construction to author-defined inputs; the central claim rests on quantitative tables comparing against independent methods rather than self-referential definitions or self-citation chains. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is empirical and relies on standard deep-learning training assumptions plus the publicly available BEVFusion codebase and MultiCorrupt dataset; no explicit free parameters, axioms, or invented physical entities are described in the abstract.

pith-pipeline@v0.9.0 · 5520 in / 1066 out tokens · 39993 ms · 2026-05-13T05:50:30.617718+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 1 internal anchor

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SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

INTRODUCTION Perception in self-driving cars is predominantly dependent on two complementary sensors:cameraandLiDAR[2]. The for- mer sensor is responsible for rich visual appearance, while the latter offers the precise geometry necessary for accurate detection of objects [3]. To exploit this complementary infor- mation from camera and LiDAR, techniques fo...

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1, as underlying framework to apply our fusion module

METHODOLOGY Setup and Notation.We adopt the pipeline of BEVFusion [1], depicted in Fig. 1, as underlying framework to apply our fusion module. As in BEVFusion, we first extract fea- ture representations utilizing modality-specific encoders for the LiDAR point cloudPand for the multi-view camera im- agesI={I k}K k=1, whereKrepresents the number of camera v...

work page
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EXPERIMENTS Dataset.We evaluated our proposed strategies on the nuScenes dataset [21]. This is a large-scale multimodal benchmark dataset for autonomous driving, providing full 360° coverage, including six cameras, one32-beam LiDAR, and five radars; this study focuses on LiDAR and camera data streams, and we therefore discard the radar data. The dataset c...

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RESULTS AND DISCUSSION Robustness Against Corrupted Sensor Modalities.Ta- ble 1 provides an extensive performance evaluation of our method alongside other SOTA strategies under various types of corruptions. The experimental results demonstrate that all methods experience performance deterioration as the severity of corruptions increases, but the extent of...

work page
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CONCLUSION In this work, we proposed SB-BEVFusion method that im- proves the robustness of the model against sensor corruption and failure. The paper investigated several fusion opera- tors to effectively combine multimodal BEV representations and demonstrated that unweighted averaging provides supe- rior overall performance compared to other fusion opera...

work page
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For open access purposes, the authors have applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission

ACKNOWLEDGMENTS This research was funded in whole or in part by the Aus- trian Science Fund (FWF): Cluster of ExcellenceBilateral Artificial Intelligence(https://doi.org/10.55776/ COE12), the doc.funds.connect projectHuman-Centered Artificial Intelligence(https://doi.org/10.55776/ DFH23), and the PI projectIntent-aware Music Recom- mender Systems(https://...

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