Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps

The contemporary paradigm of scaling data annotation, crucial for developing machine learning solutions, is to hire ordinary human annotators and instruct them with expert-crafted guidelines to label data. This paradigm is laborious, tedious, and costly, motivating us to study an open problem, auto-annotation with expert-crafted guidelines (dubbed AutoExpert). We develop benchmarks by redesigning the evaluation protocol and re-annotating data with nuScenes and PandaSet, two 3D detection datasets for autonomous driving research that provide expert-crafted annotation guidelines. Their guidelines define 18 and 25 object classes, respectively, using nuanced language descriptions and a few visual examples. Following the guidelines that require using 3D cuboids to label LiDAR data, AutoExpert requires algorithms to learn on few-shot labeled images and texts to perform the task of 3D detection on LiDAR data. Apparently, the challenges of AutoExpert lie in the data-modality and task discrepancy. Nevertheless, public foundation models (FMs) serve as promising tools to tackle these challenges. To address AutoExpert, we adopt a conceptually simple pipeline consisting of three components: (1) 2D object detection and segmentation in RGB images, (2) lifting 2D detections into 3D using known sensor poses, and (3) 3D cuboids generation for the 2D detections. Within this pipeline, we enhance and evaluate a variety of methods such as open-vocabulary detectors, few-shot detectors, and self-supervised learned detectors. We also develop novel techniques, leading to refined components that boost 3D detection mAP from 12.1 to 25.4 on the AutoExpert-nuScenes benchmark.

End-to-end autonomous driving solutions, which directly process multimodal sensory data and output fine-grained control commands, have gradually become a mainstream direction with the development of autonomous driving technology. However, current methods in this category rely on single-vehicle data collection for model training and optimization, which suffers from high acquisition and annotation costs, scarcity of valuable scenarios, and data silos. To address these challenges, we propose RS2AD-LiDAR, a novel framework for reconstructing and generating vehicle-mounted LiDAR data from roadside sensor observations. Since no public dataset currently provides highly overlapping perception coverage between roadside and vehicle-mounted LiDAR sensors, which is essential for studying roadside-to-vehicle data generation, we constructed a dedicated dataset named R2V-LiDAR which is used solely for evaluation in this work. Specifically, our method transforms roadside LiDAR point clouds into the vehicle-mounted LiDAR coordinate system, and synthesizes high-fidelity vehicle-mounted data via virtual LiDAR modeling and point cloud resampling techniques. To the best of our knowledge, this is the first approach to reconstruct vehicle-mounted LiDAR data from roadside sensor inputs. Extensive experimental comparisons demonstrate the semantic similarity between the generated data and real data. Furthermore, object detection experiments show that incorporating the generated data into real data for model training improves both Bird's Eye View (BEV) and 3D detection accuracy, thereby validating the effectiveness of the proposed method.

LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory requirements. Existing approaches address robustness and resource efficiency separately, resulting in compact detectors that are fragile under corruption or robust detectors that remain costly to deploy. We therefore formulate LiDAR perception provisioning as constructing a detector that remains accurate under point-cloud corruptions while meeting constraints on inference latency and peak GPU memory. To achieve robustness, we first craft corrupted samples that mimic extreme weather conditions. Then we train candidate detectors offline by ordering clean and corrupted samples by perturbation sensitivity, through which detectors learn more from hard corrupted samples. We estimate perturbation sensitivity using an ensemble of seven heterogeneous 3D detectors by tracking changes in confidence, BEV overlap, and unmatched detections. To improve efficiency, we narrow the network and use offline neighborhood-density measurements to determine how many points and neighbors each sampling stage processes. We evaluate our method using AP (Average Precision) under clean and corrupted conditions, inference latency, and peak allocated GPU memory. On KITTI for Car detection with PointRCNN, our method improves Moderate AP under corruption by 3.32 points over the original detector while reducing mean latency by 19.8% and peak allocated GPU memory by 53.9%. Across two datasets, two detector architectures, and three object classes, our method improves AP under corruption by 4.03 points on average over the corresponding original detectors, while reducing mean inference latency and peak allocated GPU memory by 14.4% and 38.0%, respectively. Our code is available at https://github.com/drunklibai/robust-efficient-lidar-perception.