Neural Rendering for Sensor Adaptation in 3D Object Detection

2025-08-18

AI Summary

In autonomous driving, varying camera layouts across vehicle models induce significant domain shift when deploying 3D detection models across heterogeneous sensors, leading to performance degradation. To address this, we propose a neural rendering–based sensor adaptation method that introduces the first differentiable reprojection pipeline explicitly designed for camera geometric configuration discrepancies, enabling precise and learnable image-to-BEV transformation. This pipeline is jointly optimized with BEV-based detectors (e.g., BEVFormer). To rigorously evaluate cross-sensor generalization, we release CamShift—a benchmark dataset quantifying performance decay of mainstream 3D detectors under sensor shifts. Experiments demonstrate that our method substantially mitigates domain gaps: it significantly improves cross-configuration generalization across multiple models on the CamShift benchmark, reduces reliance on annotated data for new vehicle platforms, and enhances deployment flexibility of perception systems across heterogeneous vehicle fleets.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

Autonomous vehicles often have varying camera sensor setups, which is inevitable due to restricted placement options for different vehicle types. Training a perception model on one particular setup and evaluating it on a new, different sensor setup reveals the so-called cross-sensor domain gap, typically leading to a degradation in accuracy. In this paper, we investigate the impact of the cross-sensor domain gap on state-of-the-art 3D object detectors. To this end, we introduce CamShift, a dataset inspired by nuScenes and created in CARLA to specifically simulate the domain gap between subcompact vehicles and sport utility vehicles (SUVs). Using CamShift, we demonstrate significant cross-sensor performance degradation, identify robustness dependencies on model architecture, and propose a data-driven solution to mitigate the effect. On the one hand, we show that model architectures based on a dense Bird's Eye View (BEV) representation with backward projection, such as BEVFormer, are the most robust against varying sensor configurations. On the other hand, we propose a novel data-driven sensor adaptation pipeline based on neural rendering, which can transform entire datasets to match different camera sensor setups. Applying this approach improves performance across all investigated 3D object detectors, mitigating the cross-sensor domain gap by a large margin and reducing the need for new data collection by enabling efficient data reusability across vehicles with different sensor setups. The CamShift dataset and the sensor adaptation benchmark are available at https://dmholtz.github.io/camshift/.

Problem

• Addressing cross-sensor domain gap in 3D object detection for autonomous vehicles
• Evaluating robustness of 3D object detectors to varying camera sensor setups
• Proposing neural rendering to adapt datasets for different sensor configurations

Innovation

• Neural rendering adapts sensor setups
• Dense BEV representation enhances robustness
• CamShift dataset simulates cross-sensor gaps