Lotus: A diffusion-based visual foundation model for dense geometry prediction.

Predicting dense geometry in computer vision involves estimating properties such as depth and surface normals for each pixel in an image. Accurate geometry prediction is critical for applications such as robotics, autonomous driving, and augmented reality, but current methods often require extensive training on labeled data sets and struggle to generalize across diverse tasks.

Existing methods for dense geometry prediction are generally based on supervised learning approaches using convolutional neural networks (CNN) or transformer architectures. These methods require large amounts of labeled data and often do not perform well in zero-shot scenarios, where models are expected to generalize to new tasks without specific training for them. Furthermore, most current models are designed for specific geometry prediction tasks and lack the versatility to adapt to other related tasks.

To overcome these challenges, a team of researchers from HKUST (GZ), the University of Adelaide, Huawei Noah's Ark Laboratory and HKU have introduced Lotus, a novel diffusion-based visual-based model that aims to improve the prediction of High quality dense geometry. Lotus is designed to handle various geometry perception tasks, such as normal and zero-shot depth estimation, using a unified approach. Unlike traditional models that rely on task-specific architectures, Lotus leverages diffusion processes to generate visual predictions, making it more flexible and able to adapt to various dense prediction tasks without requiring extensive retraining.

Lotus is a diffusion-based visual-based model, meaning it uses a probabilistic diffusion process to generate detailed geometric predictions from visual inputs. In this model, images are transformed through a series of stages to which noise is added and then gradually removed to generate predictions of depth and surface normals. This approach allows Lotus to capture rich geometric details that often go unnoticed in conventional CNN-based models.

The researchers designed Lotus to operate in a zero-shot configuration, allowing it to generalize to new geometry prediction tasks without the need for task-specific training. This makes Lotus a versatile tool for dense visual prediction, suitable for various applications where adaptability is key. In experiments, Lotus achieved state-of-the-art (SoTA) performance on two main geometry perception tasks: zero shot depth and normal estimation. The model outperformed existing baselines, demonstrating its effectiveness in producing high-quality geometric predictions even in challenging and unseen scenarios.

In addition to achieving high performance, Lotus also comes with easy-to-use tools to explore its capabilities. The authors have launched two Gradio applications in Hugging Face Spaces, providing an interactive way for users to experiment with Lotus and see its performance with real-world data.

Overall, Lotus represents a significant advance in the field of dense geometry prediction. By leveraging a diffusion-based approach, it effectively overcomes the limitations of traditional methods and provides a flexible and powerful solution for various visual prediction tasks. Its impressive zero-shot performance highlights its potential as a visual base model for a wide range of applications.

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