LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, farm sustainability, and informed decision-making. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, deepening our understanding of genetic variation and enhancing desirable crop traits to optimize performance in various environments. There is increasing interest in using machine learning (ML) and deep learning (DL) algorithms for genotype-to-phenotype prediction due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation, which could potentially help optimize yields and management practices. Our model outperformed the other baseline methods, demonstrating its potential in handling complex high-dimensional agricultural datasets and enhancing crop phenotype prediction performance.

LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives

Large garages are ubiquitous yet intricate scenes in our daily lives, posing challenges characterized by monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction fail in these environments due to poor correspondence construction. To address these challenges, this paper introduces LetsGo, a LiDAR-assisted Gaussian splatting approach for large-scale garage modeling and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate accurate LiDAR and image data scanning. With this Polar device, we present a GarageWorld dataset consisting of five expansive garage scenes with diverse geometric structures and will release the dataset to the community for further research. We demonstrate that the collected LiDAR point cloud by the Polar device enhances a suite of 3D Gaussian splatting algorithms for garage scene modeling and rendering. We also propose a novel depth regularizer for 3D Gaussian splatting algorithm training, effectively eliminating floating artifacts in rendered images, and a lightweight Level of Detail (LOD) Gaussian renderer for real-time viewing on web-based devices. Additionally, we explore a hybrid representation that combines the advantages of traditional mesh in depicting simple geometry and colors (e.g., walls and the ground) with modern 3D Gaussian representations capturing complex details and high-frequency textures. This strategy achieves an optimal balance between memory performance and rendering quality. Experimental results on our dataset, along with ScanNet++ and KITTI-360, demonstrate the superiority of our method in rendering quality and resource efficiency.

HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet, efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper, we present HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking, achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors, with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then, we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach, which significantly outperforms existing approaches in terms of optimization speed, rendering quality, and storage overhead.

NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering

We have recently seen tremendous progress in neural rendering (NR) advances, i.e., NeRF, for photo-real free-view synthesis. Yet, as a local technique based on a single computer/GPU, even the best-engineered Instant-NGP or i-NGP cannot reach real-time performance when rendering at a high resolution, and often requires huge local computing resources. In this paper, we resort to cloud rendering and present NEPHELE, a neural platform for highly realistic cloud radiance rendering. In stark contrast with existing NR approaches, our NEPHELE allows for more powerful rendering capabilities by combining multiple remote GPUs and facilitates collaboration by allowing multiple people to view the same NeRF scene simultaneously. We introduce i-NOLF to employ opacity light fields for ultra-fast neural radiance rendering in a one-query-per-ray manner. We further resemble the Lumigraph with geometry proxies for fast ray querying and subsequently employ a small MLP to model the local opacity lumishperes for high-quality rendering. We also adopt Perfect Spatial Hashing in i-NOLF to enhance cache coherence. As a result, our i-NOLF achieves an order of magnitude performance gain in terms of efficiency than i-NGP, especially for the multi-user multi-viewpoint setting under cloud rendering scenarios. We further tailor a task scheduler accompanied by our i-NOLF representation and demonstrate the advance of our methodological design through a comprehensive cloud platform, consisting of a series of cooperated modules, i.e., render farms, task assigner, frame composer, and detailed streaming strategies. Using such a cloud platform compatible with neural rendering, we further showcase the capabilities of our cloud radiance rendering through a series of applications, ranging from cloud VR/AR rendering.