Large Language Models (LLMs) have taken the Artificial Intelligence community by storm. Their recent impact and incredible performance display have helped contribute to a wide selection of industries corresponding to healthcare, finance, entertainment, etc. The well-known LLMs like GPT-3.5, GPT 4, DALLE 2, and BERT, also referred to as the inspiration models, perform extraordinary tasks and ease our lives by generating unique content given just a brief natural language prompt.
Recent vision foundation models (VFMs) like SAM, X-Decoder, and SEEM have made many advancements in computer vision. Although VFMs have made tremendous progress in 2D perception tasks, 3D VFM research still must be improved. Researchers have suggested that expanding current 2D VFMs for 3D perception tasks is required. One crucial 3D perception task is the segmentation of point clouds captured by LiDAR sensors, which is important for the secure operation of autonomous vehicles.
Existing point cloud segmentation techniques mainly depend on sizable datasets which have been annotated for training; nonetheless, labeling point clouds is time-consuming and difficult. To beat all of the challenges, a team of researchers has introduced Seal, a framework that uses vision foundation models for segmenting diverse automotive point cloud sequences. Inspired by cross-modal representation learning, Seal gathers semantically wealthy knowledge from VFMs to support self-supervised representation learning on automotive point clouds. The predominant idea is to develop high-quality contrastive samples for cross-modal representation learning using a 2D-3D relationship between LiDAR and camera sensors.
Seal possesses three key properties: scalability, consistency, and generalizability.
- Scalability – Seal makes use of VFMs by simply converting them into point clouds, removing the need for 2D or 3D annotations in the course of the pretraining phase. As a consequence of its scalability, it manages vast amounts of information, which even helps eliminates the time-consuming need for human annotation.
- Consistency: The architecture enforces spatial and temporal links at each the camera-to-LiDAR and point-to-segment stages. Seal enables efficient cross-modal representation learning by capturing the cross-modal interactions between vision, i.e., camera and LiDAR sensors which assist in making sure that the learned representations incorporate pertinent and coherent data from each modalities.
- Generalizability: Seal enables knowledge transfer to downstream applications involving various point cloud datasets. It generalizes and handles datasets with different resolutions, sizes, degrees of cleanliness, contamination levels, actual data, and artificial data.
A number of the key contributions mentioned by the team are –
- The proposed framework Seal is a scalable, reliable, and generalizable framework created to capture semantic-aware spatial and temporal consistency.
- It allows the extraction of useful features from automobile point cloud sequences.
- The authors have stated that this study is the primary to make use of 2D vision foundation models for self-supervised representation learning on a major scale of 3D point clouds.
- Across 11 different point cloud datasets with various data configurations, SEAL has performed higher than earlier methods in each linear probing and fine-tuning for downstream applications.
For evaluation, the team has performed tests on eleven distinct point cloud datasets to evaluate Seal’s performance. The outcomes demonstrated Seal’s superiority to the present approaches. On the nuScenes dataset, Seal achieved a remarkable mean Intersection over Union (mIoU) of 45.0% after linear probing. This performance surpassed random initialization by 36.9% mIoU and outperformed previous SOTA methods by 6.1% mIoU. Seal also portrayed significant performance gains in twenty different few-shot fine-tuning tasks across all eleven tested point cloud datasets.
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Tanya Malhotra is a final yr undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and demanding considering, together with an ardent interest in acquiring recent skills, leading groups, and managing work in an organized manner.