Papers Robust 3D Object Detection
“Robust 3D Object Detection” 태그가 달린 논문 46편 · 필터 해제
RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather
Robust 3D object detection in adverse weather conditions is challenging due to sensor limitations. Although combining complementary modalities such as LiDAR and 4D RADAR has shown promise, the sparsity of these sensors b…
Robust 3D Object DetectionOpen-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment
Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving. Despite progress with LiDAR-4D radar fusion, most methods are constrained by a closed-world assumption, implicitly requir…
Robust 3D Object DetectionAutonomous DrivingGeometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups
As autonomous systems expand from capital-intensive robotaxis to cost-sensitive logistics, sensor configurations are increasingly optimized for coverage-per-cost. A prevalent sparse-view setup utilizes dual-fisheye camer…
Robust 3D Object DetectionCo-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection
In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making. However, object motion and ego-motion often induce cross-frame spatiotemporal inconsistencies in BEV-based det…
Robust 3D Object DetectionAutonomous DrivingHyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection
Cross-modal knowledge distillation has emerged as an effective strategy for integrating point cloud and image features in 3D perception tasks. However, the modality heterogeneity, spatial misalignment, and the representa…
Robust 3D Object DetectionKnowledge DistillationWeather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection
Robust 3D object detection in adverse weather is highly challenging due to the varying reliability of different sensors. While existing LiDAR-4D radar fusion methods improve robustness, they predominantly rely on fixed o…
Robust 3D Object DetectionAW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection
Robust 3D object detection under adverse weather conditions is crucial for autonomous driving. However, most existing methods simply combine all weather samples for training while overlooking data distribution discrepanc…
Robust 3D Object DetectionAutonomous DrivingData AugmentationMulti-Modal Decouple and Recouple Network for Robust 3D Object Detection
Multi-modal 3D object detection with bird's eye view (BEV) has achieved desired advances on benchmarks. Nonetheless, the accuracy may drop significantly in the real world due to data corruption such as sensor configurati…
Robust 3D Object DetectionWavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection
4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and robot perception due to its low cost and all-weather robustness. However, point-cloud-based radar representations suffer from informatio…
Robust 3D Object DetectionComputational EfficiencyAutonomous DrivingALIGN: Advanced Query Initialization with LiDAR-Image Guidance for Occlusion-Robust 3D Object Detection
Recent query-based 3D object detection methods using camera and LiDAR inputs have shown strong performance, but existing query initialization strategies,such as random sampling or BEV heatmap-based sampling, often result…
Robust 3D Object DetectionDriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving
In autonomous driving, vision-centric 3D object detection recognizes and localizes 3D objects from RGB images. However, due to high annotation costs and diverse outdoor scenes, training data often fails to cover all poss…
Robust 3D Object DetectionAutonomous DrivingImage EditingRobust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion
Denoising Diffusion Probabilistic Models (DDPMs) have shown success in robust 3D object detection tasks. Existing methods often rely on the score matching from 3D boxes or pre-trained diffusion priors. However, they typi…
Robust 3D Object DetectionRobust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs
LiDAR-based 3D sensors provide point clouds, a canonical 3D representation used in various scene understanding tasks. Modern LiDARs face key challenges in several real-world scenarios, such as long-distance or low-albedo…
Robust 3D Object DetectionScene UnderstandingPoint CloudsMoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D radar and LiDAR fusion methods based on mult…
3D Object DetectionAutonomous DrivingObjectobject-detection+2Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L
4D radar has attracted attention in autonomous driving due to its ability to enable robust 3D object detection even under adverse weather conditions. To practically deploy such technologies, it is essential to achieve re…
3D Object DetectionAutonomous DrivingGPUobject-detection+2Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection
Accurate and robust 3D object detection is essential for autonomous driving, where fusing data from sensors like LiDAR and camera enhances detection accuracy. However, sensor malfunctions such as corruption or disconnect…
3D Object DetectionAutonomous Drivingobject-detectionObject Detection+1CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
Accurate and robust 3D object detection is a critical component in autonomous vehicles and robotics. While recent radar-camera fusion methods have made significant progress by fusing information in the bird's-eye view (B…
3D Object DetectionAutonomous VehiclesBEV Segmentationobject-detection+2HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data
Autonomous systems rely on accurate 3D object detection from LiDAR data, yet most detectors are limited to a predefined set of known classes, making them vulnerable to unexpected out-of-distribution (OOD) objects. In thi…
3D Object DetectionObjectobject-detectionObject Detection+1Progressive Multi-Modal Fusion for Robust 3D Object Detection
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perform sensor fusion in a single view by proj…
3D Object DetectionAutonomous DrivingObjectobject-detection+4MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection
The rise of autonomous vehicles has significantly increased the demand for robust 3D object detection systems. While cameras and LiDAR sensors each offer unique advantages--cameras provide rich texture information and Li…
3D Object DetectionAutonomous VehiclesObjectobject-detection+2