Understanding complex 3D environments is essential for many computer vision and robotic applications, especially in highly dynamic open-scene scenarios. The 3D scene graph plays an important role in the comprehension of 3D environments. However, most existing methods for 3D scene graph generation depend on pre-specified object and relationship classes (i.e., closed vocabulary) and labeled data for training, which restricts their effectiveness in the open-scene setting. To address this issue, we propose a novel Open-Scene Understanding-oriented 3D Scene Graph (OSU-3DSG) framework that can operate without labeled training data. The OSU-3DSG framework effectively extracts visual features from RGB-D image sequences and fuses them with camera pose estimates to create accurate 3D object maps. Then, by leveraging a pre-trained Vision Language Model (VLM), it generates relational triplets and constructs 3D scene graphs in a zero-shot manner. In particular, it excels at adaptively recognizing and interpreting object relationships, making it suitable for open-world applications. Finally, we perform extensive experiments on two open-world 3D datasets, namely 3DSSG and Replica, to evaluate the effectiveness and adaptability of the OSU-3DSG framework, demonstrating its potential to pave the way for the advancement of open-scene understanding. Our code and data are published at https://github.com/YuansuHao/OSU-3DSG.

In order to improve performance and avoid overheating on mobile devices, precise thermal control with low overhead is crucial. To achieve this, we propose incorporating a performance monitoring counter (PMC)-based power model into thermal control, which enables a more accurate evaluation of the CPU’s power consumption. We demonstrate the plausibility of this approach using polynomial regression based on Moore’s Law. Additionally, we introduce a lightweight PMC sampling method that can collect multiple PMCs at once in the kernel space, reducing sampling overhead. By replacing the utilization-based model in the original the intelligent power allocation (IPA) with a PMC-based power model, we realize the PMC-based IPA governor can be ported to real mobile devices. After updating the thermal control governor in the Linux kernel, we perform tests on our PMC-based IPA using a mobile phone device. We compare it with Stepwise and IPA, which are commonly used in current mobile phone systems. We choose the CPU-intensive workbench, I/O-intensive workbench, and CPU and I/O-intensive hybrid workbench as workloads. The results show that PMC-based IPA effectively reduces energy consumption while improving performance. In particular, during the CPU and I/O-intensive hybrid experiment, where CPU-intensive and I/O-intensive tasks are executed alternately, PMC-based IPA reduces the running time by 10.0% and energy consumption by 16.6% compared to the original IPA. In order to verify the benefits of PMC-based IPA, mobile phone testing software AI Bench and Antutu are utilized. The results show that our scheme is able to control temperature more precisely than IPA and achieves a better score while consuming less energy, particularly during AI computing. These experiment results suggest that PMC-based IPA is valuable for practical use.