3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and ignore the strong prior provided by the evolution of 3D scenes (e.g., only dynamic objects move). In this paper, we propose a world-model-based framework to exploit the scene evolution for perception. We reformulate 3D occupancy prediction as a 4D occupancy forecasting problem conditioned on the current sensor input. We decompose the scene evolution into three factors: 1) ego motion alignment of static scenes; 2) local movements of dynamic objects; and 3) completion of newly-observed scenes. We then employ a Gaussian world model (GaussianWorld) to explicitly exploit these priors and infer the scene evolution in the 3D Gaussian space considering the current RGB observation. We evaluate the effectiveness of our framework on the widely used nuScenes dataset. Our GaussianWorld improves the performance of the single-frame counterpart by over 2% in mIoU without introducing additional computations. Code: https://github.com/zuosc19/GaussianWorld.

3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline perception from one or a few views and cannot be applied to embodied agents that demand to gradually perceive the scene through progressive embodied exploration. In this paper, we formulate an embodied 3D occupancy prediction task to target this practical scenario and propose a Gaussian-based EmbodiedOcc framework to accomplish it. We initialize the global scene with uniform 3D semantic Gaussians and progressively update local regions observed by the embodied agent. For each update, we extract semantic and structural features from the observed image and efficiently incorporate them via deformable cross-attention to refine the regional Gaussians. Finally, we employ Gaussian-to-voxel splatting to obtain the global 3D occupancy from the updated 3D Gaussians. Our EmbodiedOcc assumes an unknown (i.e., uniformly distributed) environment and maintains an explicit global memory of it with 3D Gaussians. It gradually gains knowledge through the local refinement of regional Gaussians, which is consistent with how humans understand new scenes through embodied exploration. We reorganize an EmbodiedOcc-ScanNet benchmark based on local annotations to facilitate the evaluation of the embodied 3D occupancy prediction task. Our EmbodiedOcc outperforms existing methods by a large margin and accomplishes the embodied occupancy prediction with high accuracy and efficiency. Code: https://github.com/YkiWu/EmbodiedOcc.

3D occupancy prediction is crucial for robust autonomous driving systems as it enables comprehensive perception of environmental structures and semantics. Most existing methods employ dense voxel-based scene representations, ignoring the sparsity of driving scenes and resulting in inefficiency. Recent works explore object-centric representations based on sparse Gaussians, but their ellipsoidal shape prior limits the modeling of diverse structures. In real-world driving scenes, objects exhibit rich geometries (e.g., cuboids, cylinders, and irregular shapes), necessitating excessive ellipsoidal Gaussians densely packed for accurate modeling, which leads to inefficient representations. To address this, we propose to use geometrically expressive superquadrics as scene primitives, enabling efficient representation of complex structures with fewer primitives through their inherent shape diversity. We develop a probabilistic superquadric mixture model, which interprets each superquadric as an occupancy probability distribution with a corresponding geometry prior, and calculates semantics through probabilistic mixture. Building on this, we present QuadricFormer, a superquadric-based model for efficient 3D occupancy prediction, and introduce a pruning-and-splitting module to further enhance modeling efficiency by concentrating superquadrics in occupied regions. Extensive experiments on the nuScenes and KITTI-360 datasets demonstrate that QuadricFormer achieves state-of-the-art performance while maintaining superior efficiency. Code is available at https://github.com/zuosc19/QuadricFormer.

Modern processors provide hundreds of low-level hardware events (such as cache miss rate), but offer only a small number (usually 6-12) of hardware counters to collect these events due to limited register resource. Multiplexing (MPX) is an estimation-based technique designed to collect hardware events simultaneously with few hardware counters. However, the low-accuracy of existing MPX methods prevents this technique from wide usage in real conditions. To obtain accurate and reliable hardware counter values, we conducted this work in three steps: 1) to explore the root cause of inaccuracy, we characterized the estimation errors of MPX, and found that estimation errors arise from the outliers in PAPI; 2) to eliminate these outliers and improve MPX accuracy, we proposed two non-linear growth rate gradient estimation methods: divided curved-area method (DCAM) and curved-area method (CAM); 3) based on these two methods, we developed a new MPX library for PAPI, NeoMPX. We evaluated NeoMPX with six Rodinia benchmarks on four mainstream x86 and ARM server processors, and compared the results with PAPI default MPX and two other state-of-art MPX methods, DIRA, and TAM. Evaluations show that, for collecting 16 evaluated hardware events, our methods can improve up to 59% accuracy than PAPI default MPX, and achieve 36% and 5% higher accuracy than DIRA and TAM, respectively. We have open-sourced NeoMPX and expect it to enable PAPI MPX for practical usage.

The ARM-based server processors have been gaining momentum in high performance computing (HPC). While not designed specifically for HPC, Huawei Kunpeng 916 processor has 32 ARMv8 cores and is tempting for HPC workloads. However, its potential remains unknown. To throughly understand the potential, we conducted a systematic evaluation in three steps by using: 1) three well-known benchmarks (HPL, STREAM, and LMbench); 2) three typical scientific kernels (SpMV, N-body, and GEMM); 3) three widely used mini-apps (TeaLeaf, Neutral, and SNAP) and a real-world application GTC-P. We compared the performance results of Kunpeng 916 with that of Intel Xeon E5-2680v3/4 (Haswell/Broadwell). The evaluation results show that Kunpeng 916 has higher memory bandwidth than the two Intel processors, thus it can achieve compelling performance for running memory bound HPC applications.