BloomNet: Perception of Blooming Effect in ADAS using Synthetic LiDAR Point Cloud Data

Integrating multi-modal sensor capabilities is imperative in the current landscape of technological advancements aimed at achieving fully autonomous driving systems. LiDAR sensors are pivotal in demonstrating exceptional reliability in adverse weather conditions, day-night scenarios, and various complex situations because of their laser pulse emission properties. LiDAR predicts object distance with remarkable precision by leveraging time-of-flight measurements from laser pulse reflection. However, challenges arise when laser pulses encounter highly reflective surfaces, leading to a phenomenon known as Blooming. Especially on high reflectors, blooming poses a significant issue as it can obscure the accurate determination of an object’s dimension. This can impact the performance of object detection and classification algorithms in autonomous driving systems. More comprehensive LiDAR-Blooming datasets and straightforward algorithms must be developed in state-of-the-art research to effectively perceive and understand the blooming effect in real-time. In response to this challenge, our paper proposes a novel algorithm designed to generate and validate synthetic blooming datasets, offering a comprehensive understanding of the LiDAR-based phenomenon. Furthermore, we introduce an advanced deep-learning model named BloomNet, which addresses LiDAR-blooming issues. Experiments are conducted with state-of-the-art models, and our proposed model, BloomNet, outperforms existing approaches by huge margins. The results in our artificially created synthetic dataset and real-time blooming scenarios are also promising.