Core Viewpoint - The article presents AutoOcc, an innovative framework for automatic open-ended 3D semantic occupancy annotation that surpasses existing methods without requiring human labeling, showcasing excellent generalization capabilities [5][11][26]. Summary by Sections Introduction - AutoOcc is developed by the VDIG laboratory at Peking University, led by researchers Zhou Xiaoyu and Wang Yongtao, and has been recognized in top conferences and competitions in the computer vision field [2][4]. Problem Statement - The challenge of generating accurate and complete semantic occupancy annotations from raw sensor data at low cost remains significant in the fields of autonomous driving and embodied intelligence [5][8]. Methodology - AutoOcc utilizes a vision-language model (VLM) to create semantic attention maps for scene description and dynamically expands the semantic list, while a self-estimating optical flow module identifies and processes dynamic objects in temporal rendering [5][11][17]. Key Innovations - The framework introduces a 3D Gaussian representation (VL-GS) that effectively models complete 3D geometry and semantics in driving scenarios, demonstrating superior representation efficiency, accuracy, and perception capabilities [6][17]. Experimental Results - Extensive experiments indicate that AutoOcc outperforms existing automated 3D semantic occupancy annotation methods and exhibits remarkable zero-shot generalization across datasets [7][21][22]. Comparison with Existing Methods - AutoOcc is compared with traditional methods that rely on human labeling and extensive post-processing, highlighting its speed and open-ended semantic annotation capabilities [14][21]. Performance Metrics - The framework shows significant advantages in terms of robustness and open semantic labeling ability, achieving state-of-the-art performance in both specific semantic categories and across datasets [20][21]. Efficiency Evaluation - AutoOcc demonstrates a notable reduction in computational costs while enhancing annotation performance, achieving a balance between efficiency and flexibility without relying on human annotations [24][25]. Conclusion - The article concludes that AutoOcc represents a significant advancement in automated open semantic 3D occupancy annotation, integrating visual language model guidance with differentiable 3D Gaussian techniques [26].