Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360° panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360° panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object DetectionAutonomous VehiclesDepth EstimationMonocular Depth Estimationobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow
The proposed method uses live image footage which, based on calculations of pixel motion, decides whether or not an object is in the blind-spot. If found, the driver is notified by a sensory light or noise built into the…
CPUOptical Flow EstimationPositionBlind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture
Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other …
object-detectionObject Detectionvehicle detectionEliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human dri…
3D Object DetectionAutonomous VehiclesDepth EstimationMonocular Depth Estimation+2TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
Blind-spot networks (BSNs) enable self-supervised image denoising by preventing access to the target pixel, allowing clean signal estimation without ground-truth supervision. However, this approach assumes pixel-wise noi…
Knowledge DistillationImage DenoisingCan you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous methods that rely on geometric approximations,…
Autonomous Vehicles