Camera-LiDAR Fusion

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Camera–LiDAR fusion studies how to combine cameras’ rich appearance cues with LiDAR’s metric 3D geometry for tasks such as object detection, segmentation, depth completion, occupancy prediction, tracking, and localization. Common approaches project sensor data into a shared image or bird’s-eye-view space, fuse features through attention or gating, or match observations using explicit geometric constraints; sparse LiDAR also serves as a depth prompt for camera models. Methods are commonly evaluated on driving benchmarks such as nuScenes, KITTI, Waymo, and SemanticKITTI, with growing attention to adverse weather, long range, and real-time deployment. A central challenge is making fusion reliable when calibration is imperfect or one sensor is degraded, without sacrificing accuracy, efficiency, or useful modality-specific information.

Latest papers

106 papers11 papers in the last four weeks, down 8% on the four weeks before. 0.1% of all new papers.

Mar 5, 2026

Post Fusion Bird's Eye View Feature Stabilization for Robust Multimodal 3D Detection

Camera-LiDAR fusion is widely used in autonomous driving to enable accurate 3D object detection. However, bird's-eye view (BEV) fusion detectors can degrade significantly under domain shift and sensor failures, limiting reliability in real-world deployment. Existing robustness approaches often require modifying the fusion architecture or retraining specialized models, making them difficult to integrate into already deployed systems. We propose a Post Fusion Stabilizer (PFS), a lightweight module that operates on intermediate BEV representations of existing detectors and produces a refined feature map for the original detection head. The design stabilizes feature statistics under domain shift, suppresses spatial regions affected by sensor degradation, and adaptively restores weakened cues through residual correction. Designed as a near-identity transformation, PFS preserves performance while improving robustness under diverse camera and LiDAR corruptions. Evaluations on the nuScenes benchmark demonstrate that PFS achieves state-of-the-art results in several failure modes, notably improving camera dropout robustness by +1.2% and low-light performance by +4.4% mAP while maintaining a lightweight footprint of only 3.3 M parameters.

Trung Tien Dong, Dev Thakkar, Arman Sargolzaei +1

College of Engineering, University of South Florida, Tampa, FL 33620, USA

Multimodal Robustness 3D Object Detection Camera-LiDAR Fusion Distribution Shift

Mar 5, 2026

Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation

Nowadays, an increasing number of works fuse LiDAR and RGB data in the bird's-eye view (BEV) space for 3D object detection in autonomous driving systems. However, existing methods suffer from over-reliance on the LiDAR branch, with insufficient exploration of RGB information. To tackle this issue, we propose Fusion4CA, which is built upon the classic BEVFusion framework and dedicated to fully exploiting visual input with plug-and-play components. Specifically, a contrastive alignment module is designed to calibrate image features with 3D geometry, and a camera auxiliary branch is introduced to mine RGB information sufficiently during training. For further performance enhancement, we leverage an off-the-shelf cognitive adapter to make the most of pretrained image weights, and integrate a standard coordinate attention module into the fusion stage as a supplementary boost. Experiments on the nuScenes dataset demonstrate that our method achieves 69.7% mAP with only 6 training epochs and a mere 3.48% increase in inference parameters, yielding a 1.2% improvement over the baseline which is fully trained for 20 epochs. Extensive experiments in a simulated lunar environment further validate the effectiveness and generalization of our method. Our code will be released through Fusion4CA.

Kang Luo, Xin Chen, Yangyi Xiao +1

IRMV Lab, the Department of Automation, Shanghai Jiao Tong University.

3D Object Detection Camera-LiDAR Fusion Autonomous Driving Perception Cross-Modal Alignment

Mar 4, 2026

LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving

Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consistency, and cross-domain generalization. To address these challenges, we present DriveMVS, a novel multi-view stereo framework that reconciles these competing objectives through two key insights: (1) Sparse but metrically accurate LiDAR observations can serve as geometric prompts to anchor depth estimation in absolute scale, and (2) deep fusion of diverse cues is essential for resolving ambiguities and enhancing robustness, while a spatio-temporal decoder ensures consistency across frames. Built upon these principles, DriveMVS embeds the LiDAR prompt in two ways: as a hard geometric prior that anchors the cost volume, and as soft feature-wise guidance fused by a triple-cue combiner. Regarding temporal consistency, DriveMVS employs a spatio-temporal decoder that jointly leverages geometric cues from the MVS cost volume and temporal context from neighboring frames. Experiments show that DriveMVS achieves state-of-the-art performance on multiple benchmarks, excelling in metric accuracy, temporal stability, and zero-shot cross-domain transfer, demonstrating its practical value for scalable, reliable autonomous driving systems.

Qihao Sun, Jiarun Liu, Ziqian Ni +5

Unmanned Vehicle Dept., CaiNiao Inc., Alibaba Group · Harbin Institute of Technology

Multi-View Depth Estimation Depth Estimation Multi-View 3D Reconstruction Autonomous Driving Perception Camera-LiDAR Fusion

Mar 3, 2026

VLMFusionOcc3D: VLM Assisted Multi-Modal 3D Semantic Occupancy Prediction

This paper introduces VLMFusionOcc3D, a robust multimodal framework for dense 3D semantic occupancy prediction in autonomous driving. Current voxel-based occupancy models often struggle with semantic ambiguity in sparse geometric grids and performance degradation under adverse weather conditions. To address these challenges, we leverage the rich linguistic priors of Vision-Language Models (VLMs) to anchor ambiguous voxel features to stable semantic concepts. Our framework initiates with a dual-branch feature extraction pipeline that projects multi-view images and LiDAR point clouds into a unified voxel space. We propose Instance-driven VLM Attention (InstVLM), which utilizes gated cross-attention and LoRA-adapted CLIP embeddings to inject high-level semantic and geographic priors directly into the 3D voxels. Furthermore, we introduce Weather-Aware Adaptive Fusion (WeathFusion), a dynamic gating mechanism that utilizes vehicle metadata and weather-conditioned prompts to re-weight sensor contributions based on real-time environmental reliability. To ensure structural consistency, a Depth-Aware Geometric Alignment (DAGA) loss is employed to align dense camera-derived geometry with sparse, spatially accurate LiDAR returns. Extensive experiments on the nuScenes and SemanticKITTI datasets demonstrate that our plug-and-play modules consistently enhance the performance of state-of-the-art voxel-based baselines. Notably, our approach achieves significant improvements in challenging weather scenarios, offering a scalable and robust solution for complex urban navigation.

A. Enes Doruk, Hasan F. Ates

3D Occupancy Prediction Autonomous Driving Perception Camera-LiDAR Fusion VLMs for Autonomous Driving

Mar 2, 2026

LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization

Event cameras offer high-temporal-resolution sensing that remains reliable under high-speed motion and challenging lighting, making them promising for localization from LiDAR point clouds in GPS-denied and visually degraded environments. However, aligning sparse, asynchronous events with dense LiDAR maps is fundamentally ill-posed, as direct correspondence estimation suffers from modality gaps. We propose LEAR, a dual-task learning framework that jointly estimates edge structures and dense event-depth flow fields to bridge the sensing-modality divide. Instead of treating edges as a post-hoc aid, LEAR couples them with flow estimation through a cross-modal fusion mechanism that injects modality-invariant geometric cues into the motion representation, and an iterative refinement strategy that enforces mutual consistency between the two tasks over multiple update steps. This synergy produces edge-aware, depth-aligned flow fields that enable more robust and accurate pose recovery via Perspective-n-Point (PnP) solvers. On several popular and challenging datasets, LEAR achieves superior performance over the best prior method. The source code, trained models, and demo videos are made publicly available online.

Kuangyi Chen, Jun Zhang, Yuxi Hu +2

Institute of Visual Computing, Graz University of Technology, 8010 Graz, Austria · Neuromorphic Automation and Intelligence Lab (NAIL), School of Artificial Intelligence and Robotics, Hunan University, Changsha 410012, China

LiDAR Localization Camera-LiDAR Fusion Event-Based Vision Event-Based Optical Flow

Feb 27, 2026

Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of the tower. Our algorithm relies on camera-LiDAR sensor fusion for online detection and localization of insulators. In particular, the algorithm is based on insulator detection using a convolutional neural network, projection of LiDAR points onto the image, and filtering them using the bounding boxes. The detection pipeline is coupled with several proposed insulator localization methods based on DBSCAN, RANSAC, and PCA algorithms. The performance of the proposed online inspection algorithm and camera-LiDAR sensor fusion pipeline is demonstrated through simulation and real-world flights. In simulation, we showed that our single-flight inspection strategy can save up to 24 % of total inspection time, compared to the two-flight strategy of scanning the tower and afterwards visiting the inspection waypoints in the optimal way. In a real-world experiment, the best performing proposed method achieves a mean horizontal and vertical localization error for the insulator of 0.16 +- 0.08 m and 0.16 +- 0.11 m, respectively. Compared to the most relevant approach, the proposed method achieves more than an order of magnitude lower variance in horizontal insulator localization error.

Václav Riss, Vít Krátký, Robert Pěnička +1

Robotic Inspection Industrial Inspection UAV Navigation Camera-LiDAR Fusion

Feb 25, 2026

LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration

Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., target-free) with high accuracy is crucial for precise multi-sensor alignment. Existing learning-based calibration methods are typically designed for only a single pair of sensor modalities (i.e., a bi-modal setup). Unlike these methods, we propose LiREC-Net, a target-free, learning-based calibration network that jointly calibrates multiple sensor modality pairs, including LiDAR, RGB, and event data, within a unified framework. To reduce redundant computation and improve efficiency, we introduce a shared LiDAR representation that leverages features from both its 3D nature and projected depth map, ensuring better consistency across modalities. Trained and evaluated on established datasets, such as KITTI and DSEC, our LiREC-Net achieves competitive performance to bi-modal models and sets a new strong baseline for the tri-modal use case.

Aditya Ranjan Dash, Ramy Battrawy, René Schuster +1

RPTU – University Kaiserslautern-Landau · DFKI – German Research Center for Artificial Intelligence

Lidar-Camera Extrinsic Calibration Camera-LiDAR Fusion Multimodal Sensor Fusion

Feb 25, 2026

SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR

Scene flow estimation is an extremely important task in computer vision to support the perception of dynamic changes in the scene. For robust scene flow, learning-based approaches have recently achieved impressive results using either image-based or LiDAR-based modalities. However, these methods have tended to focus on the use of a single modality. To tackle these problems, we present a deep learning architecture, SF3D-RGB, that enables sparse scene flow estimation using 2D monocular images and 3D point clouds (e.g., acquired by LiDAR) as inputs. Our architecture is an end-to-end model that first encodes information from each modality into features and fuses them together. Then, the fused features enhance a graph matching module for better and more robust mapping matrix computation to generate an initial scene flow. Finally, a residual scene flow module further refines the initial scene flow. Our model is designed to strike a balance between accuracy and efficiency. Furthermore, experiments show that our proposed method outperforms single-modality methods and achieves better scene flow accuracy on real-world datasets while using fewer parameters compared to other state-of-the-art methods with fusion.

Rajai Alhimdiat, Ramy Battrawy, René Schuster +2

Islamic University of Gaza, Gaza, Palestine · German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

Scene Flow Estimation 3D Scene Understanding Camera-LiDAR Fusion

Feb 25, 2026

SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Prediction

High-definition (HD) maps are essential for autonomous driving, yet multi-modal fusion often suffers from inconsistency between camera and LiDAR modalities, leading to performance degradation under low-light conditions, occlusions, or sparse point clouds. To address this, we propose SEFMAP, a Subspace-Expert Fusion framework for robust multimodal HD map prediction. The key idea is to explicitly disentangle BEV features into four semantic subspaces: LiDAR-private, Image-private, Shared, and Interaction. Each subspace is assigned a dedicated expert, thereby preserving modality-specific cues while capturing cross-modal consensus. To adaptively combine expert outputs, we introduce an uncertainty-aware gating mechanism at the BEV-cell level, where unreliable experts are down-weighted based on predictive variance, complemented by a usage balance regularizer to prevent expert collapse. To enhance robustness in degraded conditions and promote role specialization, we further propose distribution-aware masking: during training, modality-drop scenarios are simulated using EMA-statistical surrogate features, and a specialization loss enforces distinct behaviors of private, shared, and interaction experts across complete and masked inputs. Experiments on nuScenes and Argoverse2 benchmarks demonstrate that SEFMAP achieves state-of-the-art performance, surpassing prior methods by +4.2% and +4.8% in mAP, respectively. SEF-MAPprovides a robust and effective solution for multi-modal HD map prediction under diverse and degraded conditions.

Haoxiang Fu, Lingfeng Zhang, Hao Li +7

National University of Singapore · Shenzhen Tsinghua International Graduate School, Tsinghua Univeristy · Xiaomi EV +4

Camera-LiDAR Fusion Multimodal Robustness HD Map Construction Mixture of Experts Uncertainty-Aware Multimodal Fusion

Feb 23, 2026

VGGT-MPR: VGGT-Enhanced Multimodal Place Recognition in Autonomous Driving Environments

In autonomous driving, robust place recognition is critical for global localization and loop closure detection. While inter-modality fusion of camera and LiDAR data in multimodal place recognition (MPR) has shown promise in overcoming the limitations of unimodal counterparts, existing MPR methods basically attend to hand-crafted fusion strategies and heavily parameterized backbones that require costly retraining. To address this, we propose VGGT-MPR, a multimodal place recognition framework that adopts the Visual Geometry Grounded Transformer (VGGT) as a unified geometric engine for both global retrieval and re-ranking. In the global retrieval stage, VGGT extracts geometrically-rich visual embeddings through prior depth-aware and point map supervision, and densifies sparse LiDAR point clouds with predicted depth maps to improve structural representation. This enhances the discriminative ability of fused multimodal features and produces global descriptors for fast retrieval. Beyond global retrieval, we design a training-free re-ranking mechanism that exploits VGGT's cross-view keypoint-tracking capability. By combining mask-guided keypoint extraction with confidence-aware correspondence scoring, our proposed re-ranking mechanism effectively refines retrieval results without additional parameter optimization. Extensive experiments on large-scale autonomous driving benchmarks and our self-collected data demonstrate that VGGT-MPR achieves state-of-the-art performance, exhibiting strong robustness to severe environmental changes, viewpoint shifts, and occlusions. Our code and data will be made publicly available.

Jingyi Xu, Zhangshuo Qi, Zhongmiao Yan +5

Shanghai Jiao Tong University. · Beijing Institute of Technology. · National University of Defense Technology.

Camera-LiDAR Fusion Autonomous Driving LiDAR Place Recognition

Feb 22, 2026

UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation

LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode. Under adverse conditions, degradation or failure of the camera sensor can significantly compromise the reliability of the perception system. To address this problem, we introduce UP-Fuse, a novel uncertainty-aware fusion framework in the 2D range-view that remains robust under camera sensor degradation, calibration drift, and sensor failure. Raw LiDAR data is first projected into the range-view and encoded by a LiDAR encoder, while camera features are simultaneously extracted and projected into the same shared space. At its core, UP-Fuse employs an uncertainty-guided fusion module that dynamically modulates cross-modal interaction using predicted uncertainty maps. These maps are learned by quantifying representational divergence under diverse visual degradations, ensuring that only reliable visual cues influence the fused representation. The fused range-view features are decoded by a novel hybrid 2D-3D transformer that mitigates spatial ambiguities inherent to the 2D projection and directly predicts 3D panoptic segmentation masks. Extensive experiments on Panoptic nuScenes, SemanticKITTI, and our introduced Panoptic Waymo benchmark demonstrate the efficacy and robustness of UP-Fuse, which maintains strong performance even under severe visual corruption or misalignment, making it well suited for robotic perception in safety-critical settings.

Rohit Mohan, Florian Drews, Yakov Miron +2

University of Freiburg, Germany · Robert Bosch GmbH, Germany · University of Haifa

Camera-LiDAR Fusion Panoptic Segmentation Uncertainty-Aware Multimodal Fusion

Feb 21, 2026

Enhancing 3D LiDAR Segmentation by Shaping Dense and Accurate 2D Semantic Predictions

Semantic segmentation of 3D LiDAR point clouds is important in urban remote sensing for understanding real-world street environments. This task, by projecting LiDAR point clouds and 3D semantic labels as sparse maps, can be reformulated as a 2D problem. However, the intrinsic sparsity of the projected LiDAR and label maps can result in sparse and inaccurate intermediate 2D semantic predictions, which in return limits the final 3D accuracy. To address this issue, we enhance this task by shaping dense and accurate 2D predictions. Specifically, we develop a multi-modal segmentation model, MM2D3D. By leveraging camera images as auxiliary data, we introduce cross-modal guided filtering to overcome label map sparsity by constraining intermediate 2D semantic predictions with dense semantic relations derived from the camera images; and we introduce dynamic cross pseudo supervision to overcome LiDAR map sparsity by encouraging the 2D predictions to emulate the dense distribution of the semantic predictions from the camera images. Experiments show that our techniques enable our model to achieve intermediate 2D semantic predictions with dense distribution and higher accuracy, which effectively enhances the final 3D accuracy. Comparisons with previous methods demonstrate our superior performance in both 2D and 3D spaces.

Xiaoyu Dong, Tiankui Xian, Wanshui Gan +1

The University of Tokyo, Tokyo, Japan · RIKEN AIP, Tokyo, Japan

Multimodal Semantic Segmentation 3D Semantic Segmentation Semantic Segmentation Camera-LiDAR Fusion

Feb 19, 2026

A Multi-modal Detection System for Infrastructure-based Freight Signal Priority

Freight vehicles approaching signalized intersections require reliable detection and motion estimation to support infrastructure-based Freight Signal Priority (FSP). Accurate and timely perception of vehicle type, position, and speed is essential for enabling effective priority control strategies. This paper presents the design, deployment, and evaluation of an infrastructure-based multi-modal freight vehicle detection system integrating LiDAR and camera sensors. A hybrid sensing architecture is adopted, consisting of an intersection-mounted subsystem and a midblock subsystem, connected via wireless communication for synchronized data transmission. The perception pipeline incorporates both clustering-based and deep learning-based detection methods with Kalman filter tracking to achieve stable real-time performance. LiDAR measurements are registered into geodetic reference frames to support lane-level localization and consistent vehicle tracking. Field evaluations demonstrate that the system can reliably monitor freight vehicle movements at high spatio-temporal resolution. The design and deployment provide practical insights for developing infrastructure-based sensing systems to support FSP applications.

Ziyan Zhang, Chuheng Wei, Xuanpeng Zhao +6

Center for Environmental Research and Technology (CE-CERT), University of California at Riverside (UCR), Riverside, CA

Intelligent Transportation Systems Object Detection Camera-LiDAR Fusion

Jul 26, 2025

Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo

While accurate LiDAR depth has been shown to improve stereo matching, high-end LiDAR remains costly and difficult to deploy at scale, motivating guidance from sparse LiDAR measurements. In this paper, we revisit how extremely sparse LiDAR can guide iterative stereo by examining its cost-volume retrieval mechanism. Our analysis shows that LiDAR guidance degrades sharply when only a few hundred points are available, and we provide a signal-processing explanation for why iterative stereo fails to fully exploit such sparse input. This insight leads to a simple yet highly effective remedy: pre-filling the initial disparity map so that cost-volume retrieval becomes more reliable under extremely sparse LiDAR. We find that pre-filling is also effective when injecting LiDAR depth into image features via early fusion, but for a fundamentally different reason, necessitating a distinct pre-filling approach. Combining both forms of guidance, our proposed Guided RAFT-Stereo (GRAFT-Stereo) achieves substantial improvements over existing LiDAR-guided stereo methods across multiple datasets.

Jinsu Yoo, Sooyoung Jeon, Zanming Huang +2

The Ohio State University

Camera-LiDAR Fusion Stereo Matching

Dec 18, 2023

Long-Tailed 3D Detection via Multi-Modal Fusion

Contemporary autonomous vehicle (AV) benchmarks have significantly advanced multimodal (LiDAR+RGB) 3D detection. However, despite the naturally long-tailed distribution of object classes, existing benchmarking protocols primarily focus on frequent categories (e.g., pedestrian and car), largely overlooking rare but safety-critical classes such as stroller and emergency vehicle. In practice, reliable detection of both common and rare classes is essential for safe autonomous driving. We formalize this problem as Long-Tailed 3D Detection (LT3D), where evaluation encompasses all annotated classes, including rare ones. To address LT3D, we introduce hierarchical losses that promote feature sharing across classes, diagnostic metrics that assign partial credit to semantically reasonable mistakes with respect to the semantic hierarchy (e.g., confusing a child with an adult), and a multimodal late-fusion (MMLF) framework to fuse detections. In particular, we show that rare-class accuracy benefits substantially from MMLF of independently trained uni-modal LiDAR and RGB detectors. Because of the modular design, unlike prevailing end-to-end trained multi-modal detectors that require paired LiDAR-RGB data, MMLF enables the use of advanced unimodal detectors that are trained on large-scale uni-modal datasets with sufficient data for rare classes. Lastly, we examine three fundamental design choices in MMLF, including the RGB detector representation (2D vs. 3D), cross-modal association (3D vs. image plane), and fusion strategy. We find that 2D RGB detectors recognize rare classes more reliably than 3D RGB detectors, image-plane association is more robust to depth estimation errors, and probabilistic score-calibrated fusion consistently yields the best performance. Extensive experiments on nuScenes and Argoverse2 demonstrate substantial improvements of MMLF, establishing a new state of the art.

Yechi Ma, Neehar Peri, Achal Dave +3

Department of Computer Science, Zhejiang University. · Computing & Mathematical Sciences, Caltech. · Toyota Research Institute. +2

3D Object Detection Long-Tail Autonomous Driving Multimodal Object Detection Camera-LiDAR Fusion

Jun 21, 2021

EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we project point clouds to the camera coordinate using perspective projection, and process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU. Our source code is available at https://github.com/ICEORY/PMF.

Mingkui Tan, Zhuangwei Zhuang, Sitao Chen +4

School of Software Engineering, South China University of Technology · Pazhou Laboratory, Guangzhou, China · School of Electronic and Information Engineering, South China University of Technology +2

Camera-LiDAR Fusion Autonomous Driving Perception 3D Semantic Segmentation Multimodal Sensor Fusion