We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent Large Reconstruction Models (LRMs) that achieve state-of-the-art sparse-view reconstruction quality. However, existing LRMs struggle to reconstruct unseen parts accurately and cannot recover glossy appearance or generate relightable 3D contents that can be consumed by standard Graphics engines. To address these limitations, we make three key technical contributions to build a more practical multi-view 3D reconstruction framework. First, we introduce an update model that allows us to progressively add more input views to improve our reconstruction. Second, we propose a hexa-plane neural SDF representation to better recover detailed textures, geometry and material parameters. Third, we develop a novel neural directional-embedding mechanism to handle view-dependent effects. Trained on a large-scale shape and material dataset with a tailored coarse-to-fine training scheme, our model achieves compelling results. It compares favorably to optimization-based dense-view inverse rendering methods in terms of geometry and relighting accuracy, while requiring only a fraction of the inference time.

Exponential growth in the number of subscribers intrigued Mobile Network Operators(MNOs) to invest substantial efforts towards the faster transition to IPv6 address. The current solution deployed by leading MNOs uses Dual IP 464XLAT to achieve address translation. There has been significant research on the dual IP system. However, an important area of study which is not investigated in detail is the correlation of 464XLAT in the multi-core architecture. We investigate the effects of this architecture on bandwidth utilization of mobile Smartphones with emphasis on multi-core scheduling algorithm. In this paper, we propose a novel networking packet scheduling scheme called CAA - CLAT Aware Affinity Scheduler for Next Generation Mobile Networks. CAA classifies the packets according to the characteristics and efficiently schedules among the CPU cores at its best effort for improved throughput in Dual Stack Smartphones. We also propose CAA-LITE, a lightweight version where the affinity scheduling is static with minimal steps. To illustrate the effectiveness of our proposed method, we conducted simulations in our lab at Samsung R&D India Bangalore and live air experiments in Samsung Electronics, South Korea. Our live air experiments show that the CAA outperforms the legacy by improving the throughput of around 90% under various operational conditions consistently. Moreover, our power consumption test shows that the CAA improves the power by 22% compared to original approaches.