CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality

High-quality environment lighting is the foundation of creating immersive user experiences in mobile augmented reality (AR) applications. However, achieving visually coherent environment lighting estimation for Mobile AR is challenging due to several key limitations associated with AR device sensing capabilities, including limitations in device camera FoV and pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address their key limitations of generation hallucination and slow inference process. To do so, in this work, we design and implement a generative lighting estimation system called CleAR that can produce high-quality and diverse environment maps in the format of 360$^\circ$ images. Specifically, we design a two-step generation pipeline guided by AR environment context data to ensure the results follow physical environment visual context and color appearances. To improve the estimation robustness under different lighting conditions, we design a real-time refinement component to adjust lighting estimation results on AR devices. To train and test our generative models, we curate a large-scale environment lighting estimation dataset with diverse lighting conditions. Through quantitative evaluation and user study, we show that CleAR outperforms state-of-the-art lighting estimation methods on both estimation accuracy and robustness. Moreover, CleAR supports real-time refinement of lighting estimation results, ensuring robust and timely environment lighting updates for AR applications. Our end-to-end generative estimation takes as fast as 3.2 seconds, outperforming state-of-the-art methods by 110x.

SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds

We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the latest G-PCC test model (TMC13v23) under the MPEG common test conditions (CTCs), the proposed method achieved an average Bjontegaard delta bitrate reduction of 24.58% for the Y component (21.23% for YUV combined) on the MPEG Category Solid dataset and 22.48% for the Y component (17.19% for YUV combined) on the MPEG Category Dense dataset. This is the first instance of a learning-based codec outperforming the G-PCC standard on these datasets under the MPEG CTCs.

HybridDepth: Robust Depth Fusion for Mobile AR by Leveraging Depth from Focus and Single-Image Priors

We propose HYBRIDDEPTH, a robust depth estimation pipeline that addresses the unique challenges of depth estimation for mobile AR, such as scale ambiguity, hardware heterogeneity, and generalizability. HYBRIDDEPTH leverages the camera features available on mobile devices. It effectively combines the scale accuracy inherent in Depth from Focus (DFF) methods with the generalization capabilities enabled by strong single-image depth priors. By utilizing the focal planes of a mobile camera, our approach accurately captures depth values from focused pixels and applies these values to compute scale and shift parameters for transforming relative depths into metric depths. We test our pipeline as an end-to-end system, with a newly developed mobile client to capture focal stacks, which are then sent to a GPU-powered server for depth estimation. Through comprehensive quantitative and qualitative analyses, we demonstrate that HYBRIDDEPTH not only outperforms state-of-the-art (SOTA) models in common datasets (DDFF12, NYU Depth v2) and a real-world AR dataset ARKitScenes but also demonstrates strong zero-shot generalization. For example, HYBRIDDEPTH trained on NYU Depth v2 achieves comparable performance on the DDFF12 to existing models trained on DDFF12. it also outperforms all the SOTA models in zero-shot performance on the ARKitScenes dataset. Additionally, we conduct a qualitative comparison between our model and the ARCore framework, demonstrating that our models output depth maps are significantly more accurate in terms of structural details and metric accuracy. The source code of this project is available at github.

Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow

Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for stock return forecasting with financial newsflow. In quantitative investing, return forecasting is fundamental for subsequent tasks like stock picking, portfolio optimization, etc. We formulate the model to include text representation and forecasting modules. We propose to compare the encoder-only and decoder-only LLMs, considering they generate text representations in distinct ways. The impact of these different representations on forecasting performance remains an open question. Meanwhile, we compare two simple methods of integrating LLMs' token-level representations into the forecasting module. The experiments on real news and investment universes reveal that: (1) aggregated representations from LLMs' token-level embeddings generally produce return predictions that enhance the performance of long-only and long-short portfolios; (2) in the relatively large investment universe, the decoder LLMs-based prediction model leads to stronger portfolios, whereas in the small universes, there are no consistent winners. Among the three LLMs studied (DeBERTa, Mistral, Llama), Mistral performs more robustly across different universes; (3) return predictions derived from LLMs' text representations are a strong signal for portfolio construction, outperforming conventional sentiment scores.

CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework

This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresses the key challenge of high carbon cost associated with NAS by exploring the carbon emission variations of energy and energy differences of different NAS algorithms. At the high level, CE-NAS leverages a reinforcement-learning agent to dynamically adjust GPU resources based on carbon intensity, predicted by a time-series transformer, to balance energy-efficient sampling and energy-intensive evaluation tasks. Furthermore, CE-NAS leverages a recently proposed multi-objective optimizer to effectively reduce the NAS search space. We demonstrate the efficacy of CE-NAS in lowering carbon emissions while achieving SOTA results for both NAS datasets and open-domain NAS tasks. For example, on the HW-NasBench dataset, CE-NAS reduces carbon emissions by up to 7.22X while maintaining a search efficiency comparable to vanilla NAS. For open-domain NAS tasks, CE-NAS achieves SOTA results with 97.35% top-1 accuracy on CIFAR-10 with only 1.68M parameters and a carbon consumption of 38.53 lbs of CO2. On ImageNet, our searched model achieves 80.6% top-1 accuracy with a 0.78 ms TensorRT latency using FP16 on NVIDIA V100, consuming only 909.86 lbs of CO2, making it comparable to other one-shot-based NAS baselines.

Multi-objective Neural Architecture Search by Learning Search Space Partitions

Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This results in deep learning model designers leveraging multi-objective optimization to design effective deep neural networks in multiple criteria. However, applying multi-objective optimizations to neural architecture search (NAS) is nontrivial because NAS tasks usually have a huge search space, along with a non-negligible searching cost. This requires effective multi-objective search algorithms to alleviate the GPU costs. In this work, we implement a novel multi-objectives optimizer based on a recently proposed meta-algorithm called LaMOO on NAS tasks. In a nutshell, LaMOO speedups the search process by learning a model from observed samples to partition the search space and then focusing on promising regions likely to contain a subset of the Pareto frontier. Using LaMOO, we observe an improvement of more than 200% sample efficiency compared to Bayesian optimization and evolutionary-based multi-objective optimizers on different NAS datasets. For example, when combined with LaMOO, qEHVI achieves a 225% improvement in sample efficiency compared to using qEHVI alone in NasBench201. For real-world tasks, LaMOO achieves 97.36% accuracy with only 1.62M #Params on CIFAR10 in only 600 search samples. On ImageNet, our large model reaches 80.4% top-1 accuracy with only 522M #FLOPs.

Mobile AR Depth Estimation: Challenges & Prospects -- Extended Version

Metric depth estimation plays an important role in mobile augmented reality (AR). With accurate metric depth, we can achieve more realistic user interactions such as object placement and occlusion detection. While specialized hardware like LiDAR demonstrates its promise, its restricted availability, i.e., only on selected high-end mobile devices, and performance limitations such as range and sensitivity to the environment, make it less ideal. Monocular depth estimation, on the other hand, relies solely on mobile cameras, which are ubiquitous, making it a promising alternative for mobile AR. In this paper, we investigate the challenges and opportunities of achieving accurate metric depth estimation in mobile AR. We tested four different state-of-the-art monocular depth estimation models on a newly introduced dataset (ARKitScenes) and identified three types of challenges: hard-ware, data, and model related challenges. Furthermore, our research provides promising future directions to explore and solve those challenges. These directions include (i) using more hardware-related information from the mobile device's camera and other available sensors, (ii) capturing high-quality data to reflect real-world AR scenarios, and (iii) designing a model architecture to utilize the new information.

Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.

Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting

In many data-driven applications, collecting data from different sources is increasingly desirable for enhancing performance. In this paper, we are interested in the problem of probabilistic forecasting with multi-source time series. We propose a neural mixture structure-based probability model for learning different predictive relations and their adaptive combinations from multi-source time series. We present the prediction and uncertainty quantification methods that apply to different distributions of target variables. Additionally, given the imbalanced and unstable behaviors observed during the direct training of the proposed mixture model, we develop a phased learning method and provide a theoretical analysis. In experimental evaluations, the mixture model trained by the phased learning exhibits competitive performance on both point and probabilistic prediction metrics. Meanwhile, the proposed uncertainty conditioned error suggests the potential of the mixture model's uncertainty score as a reliability indicator of predictions.

LitAR: Visually Coherent Lighting for Mobile Augmented Reality

An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support reflective rendering, existing methods have leveraged deep learning models to estimate or have used physical light probes to capture physical lighting, typically represented in the form of an environment map. However, these methods often fail to provide visually coherent details or require additional setups. For example, the commercial framework ARKit uses a convolutional neural network that can generate realistic environment maps; however the corresponding reflective rendering might not match the physical environments. In this work, we present the design and implementation of a lighting reconstruction framework called LitAR that enables realistic and visually-coherent rendering. LitAR addresses several challenges of supporting lighting information for mobile AR. First, to address the spatial variance problem, LitAR uses two-field lighting reconstruction to divide the lighting reconstruction task into the spatial variance-aware near-field reconstruction and the directional-aware far-field reconstruction. The corresponding environment map allows reflective rendering with correct color tones. Second, LitAR uses two noise-tolerant data capturing policies to ensure data quality, namely guided bootstrapped movement and motion-based automatic capturing. Third, to handle the mismatch between the mobile computation capability and the high computation requirement of lighting reconstruction, LitAR employs two novel real-time environment map rendering techniques called multi-resolution projection and anchor extrapolation. These two techniques effectively remove the need of time-consuming mesh reconstruction while maintaining visual quality.