Procedia Computer Science · 2021 · 53 citations · 29 references
ComputingRobotics EngineeringComputer VisionRobot VisionSensor Fusion3D ReconstructionDepth EstimationMulti-view GeometryPoint Cloud ProcessingScene ReconstructionScene Understanding3D Object Detection3D Semantic SegmentationCamera FusionDepth CompletionLidar FusionLidar SensingScene ContextSemantic Information
Recently, two types of common sensors, LiDAR and Camera, show significant performance on all tasks in 3D vision. LiDAR provides accurate 3D geometry structure, while camera captures more scene context and semantic information. The fusion of two different sensor becomes a fundamental and common idea to achieve better performance. To give a thorough cognition of the complementary and boosting about two kind of sensors. This paper briefly reviews the fusion and enhancement systems between both two sensors in the field of depth completion, 3D object detection, 2D\3D semantic segmentation and 3D object tracking. Meanwhile, the state of art fusion algorithms is quantitatively demonstrated, in this paper, based on the in KITTI widely-used public dataset. Furthermore, the technical challenge and the future potential of LiDAR and camera fusion are also discussed.
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