May 25, 2026 - Company - Stories - Technology - AI & Robotics - R&D - Circular Economy May 28, 2026 Company / Press Releases Osaka, Japan, May 28, 2026 – Panasonic Holdings Corporation (Panasonic HD) today announced that two of its research papers have been accepted for presentation at CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition) 2026, one of the world’s leading international conferences in AI and computer vision. One of the two papers was also selected as a “Highlight” in recognition of its outstanding quality. The company will present the papers at the conference scheduled to be held in Colorado, United States, from June 3 to June 7, 2026. This paper presents a technology for efficiently compressing 3D spatial information, enabling both reduced information processing requirements and high spatial recognition capability. The technology is expected to contribute to advances in robotics and physical AI, where AI systems operate in the real world. In recent years, growing attention has been paid to physical AI, which enables robots and machines to recognize real-world environments, make decisions, and act autonomously. Achieving this requires advanced spatial recognition capabilities, such as understanding positional relationships among objects, and further progress in multimodal AI*1 is therefore highly anticipated. However, conventional multimodal AI-based spatial recognition technologies tend to require increasing computational cost in order to retain spatial information. By combining highly efficient compression of feature representations through clustering with staged spatial recognition learning, this technology reduces the amount of spatial information handled by multimodal AI while delivering spatial recognition performance that is equal to or better than other methods*2. For example, while some conventional 3D spatial recognition methods input approximately 8,000 tokens of spatial information into multimodal AI, this technology represents 3D space using 700 tokens. It is expected to support practical applications across