PIXOR is a real-time 3D object detection method that processes point cloud data from LiDAR sensors. It is designed for autonomous driving applications where fast and accurate detection is crucial for safety. The method uses a Bird's Eye View (BEV) representation of the scene, which allows for efficient computation and preserves the metric space necessary for accurate object detection. PIXOR is a single-stage, proposal-free detector that directly predicts 3D object estimates from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are specifically designed to balance high accuracy with real-time efficiency. The method is validated on two datasets: the KITTI BEV object detection benchmark and a large-scale 3D vehicle detection benchmark. On both datasets, PIXOR outperforms other state-of-the-art methods in terms of Average Precision (AP), achieving over 28 FPS. The method is also tested on a new large-scale dataset, TOR4D, where it shows strong performance and generalization ability. The network architecture is fully convolutional, allowing for efficient dense predictions. The method uses a multi-task header network that handles both object classification and localization. The detector uses a simple yet effective approach to encode oriented bounding boxes, which are decoded from pixel-wise predictions. The method is trained with a combination of classification and regression losses, and it uses a focal loss to handle class imbalance. The method is evaluated on multiple metrics, including AP at 0.7 IoU, and it shows superior performance compared to other methods. The method is also tested on a new dataset, TOR4D, where it outperforms a baseline detector by 3.9% in AP. The method is efficient, simple, and generalizable, making it suitable for real-time 3D object detection in autonomous driving.PIXOR is a real-time 3D object detection method that processes point cloud data from LiDAR sensors. It is designed for autonomous driving applications where fast and accurate detection is crucial for safety. The method uses a Bird's Eye View (BEV) representation of the scene, which allows for efficient computation and preserves the metric space necessary for accurate object detection. PIXOR is a single-stage, proposal-free detector that directly predicts 3D object estimates from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are specifically designed to balance high accuracy with real-time efficiency. The method is validated on two datasets: the KITTI BEV object detection benchmark and a large-scale 3D vehicle detection benchmark. On both datasets, PIXOR outperforms other state-of-the-art methods in terms of Average Precision (AP), achieving over 28 FPS. The method is also tested on a new large-scale dataset, TOR4D, where it shows strong performance and generalization ability. The network architecture is fully convolutional, allowing for efficient dense predictions. The method uses a multi-task header network that handles both object classification and localization. The detector uses a simple yet effective approach to encode oriented bounding boxes, which are decoded from pixel-wise predictions. The method is trained with a combination of classification and regression losses, and it uses a focal loss to handle class imbalance. The method is evaluated on multiple metrics, including AP at 0.7 IoU, and it shows superior performance compared to other methods. The method is also tested on a new dataset, TOR4D, where it outperforms a baseline detector by 3.9% in AP. The method is efficient, simple, and generalizable, making it suitable for real-time 3D object detection in autonomous driving.