ABSTRACT Depth estimation has been widely applied in the field of computer vision, primarily using unsupervised deep neural networks, which often rely on deeper neural networks. However, the addition of layers can result in slower convergence and suboptimal performance. To overcome these issues, we introduce a novel architecture employing model distillation, wherein a teacher network enhances the learning process of a preceding student network. To improve network speed, we integrate an ordinal module in the decoder of the teacher network for weight normalization. This module can classify weights and filter out those with the lowest information content. After the weight classification is completed, as the category value increases, the necessity of useful information decreases accordingly. Furthermore, we incorporate a residual stratification module, which adapts 2D image feature extraction methods to 3D depth, facilitating finer, multi‐scale feature representation, to expand the receptive field size at each layer of the network, thereby enhancing the accuracy and robustness of depth estimation. Experimental results using the publicly available KITTI dataset demonstrate that the proposed method accelerates network training compared to the benchmark algorithm, reducing the relative squared error by 2.3% and the root‐mean‐square error by 3.3%, thus validating the effectiveness of our approach.

Meshes are an indispensable representation in many graphics applications because they provide conformal spatial discretizations. However, mesh-based operations are often slow due to unstructured memory access patterns. We propose MeshTaichi, a novel mesh compiler that provides an intuitive programming model for efficient mesh-based operations. Our programming model hides the complex indexing system from users and allows users to write mesh-based operations using reference-style neighborhood queries. Our compiler achieves its high performance by exploiting data locality. We partition input meshes and prepare the wanted relations by inspecting users' code during compile time. During run time, we further utilize on-chip memory (shared memory on GPU and L1 cache on CPU) to access the wanted attributes of mesh elements efficiently. Our compiler decouples low-level optimization options with computations, so that users can explore different localized data attributes and different memory orderings without changing their computation code. As a result, users can write concise code using our programming model to generate efficient mesh-based computations on both CPU and GPU backends. We test MeshTaichi on a variety of physically-based simulation and geometry processing applications with both triangle and tetrahedron meshes. MeshTaichi achieves a consistent speedup ranging from 1.4× to 6×, compared to state-of-the-art mesh data structures and compilers.

High-resolution simulations can deliver great visual quality, but they are often limited by available memory, especially on GPUs. We present a compiler for physical simulation that can achieve both high performance and significantly reduced memory costs, by enabling flexible and aggressive quantization. Low-precision ("quantized") numerical data types are used and packed to represent simulation states, leading to reduced memory space and bandwidth consumption. Quantized simulation allows higher resolution simulation with less memory, which is especially attractive on GPUs. Implementing a quantized simulator that has high performance and packs the data tightly for aggressive storage reduction would be extremely labor-intensive and error-prone using a traditional programming language. To make the creation of quantized simulation practical, we have developed a new set of language abstractions and a compilation system. A suite of tailored domain-specific optimizations ensure quantized simulators often run as fast as the full-precision simulators, despite the overhead of encoding-decoding the packed quantized data types. Our programming language and compiler, based on Taichi , allow developers to effortlessly switch between different full-precision and quantized simulators, to explore the full design space of quantization schemes, and ultimately to achieve a good balance between space and precision. The creation of quantized simulation with our system has large benefits in terms of memory consumption and performance, on a variety of hardware, from mobile devices to workstations with high-end GPUs. We can simulate with levels of resolution that were previously only achievable on systems with much more memory, such as multiple GPUs. For example, on a single GPU, we can simulate a Game of Life with 20 billion cells (8× compression per pixel), an Eulerian fluid system with 421 million active voxels (1.6× compression per voxel), and a hybrid Eulerian-Lagrangian elastic object simulation with 235 million particles (1.7× compression per particle). At the same time, quantized simulations create physically plausible results. Our quantization techniques are complementary to existing acceleration approaches of physical simulation: they can be used in combination with these existing approaches, such as sparse data structures, for even higher scalability and performance.

Database driven IP geolocation is a convenient and common way to determine geographic location of an IP address. However, the underlying problem is that it is often difficult for users to determine which provider is reliable enough to meet their own scenarios. In this paper, we tackle this challenge in a data fusion perspective. We first evaluate the consistency degree of data entries among 5 free geolocation databases and employ it as an indicator of data quality assessment. We find that this indicator varies by geographic scope and granularity for a certain provider. Therefore we are able to evaluate data quality for different parts and dimensions within a database. Then a data fusion method utilizing data consistency degree and quota-based votes is proposed and analyzed. Over 40 million IP geolocation ground truth data in China, i.e., more than 10% of the total address space allocated to China, is applied to verify the effectiveness and advantage of the proposed method. In this work, we provide insights into comprehensive utilization of multi-databases characteristics for data entry fusion in the absence of enough priori knowledge.