LingBot-Depth - Ant Lingbo Technology's Open-Source Spatial Perception Model

LingWhat is Bot-Depth

LingBot-Depth is a high-precision spatial perception model open-sourced by Ant Lingbo Technology, specifically designed to solve the depth perception challenges faced by robots in scenes with transparent and reflective objects (such as glass). The model uses Masked Depth Modeling (MDM) technology to reconstruct complete depth maps from RGB images and residual depth information, and is trained on a combination of real and synthetic data, significantly improving the quality of depth output in complex scenes. The model performs excellently in tasks such as depth completion, monocular depth estimation, and robotic grasping, helping robots better understand three-dimensional environments and promoting the practical application of embodied intelligence.

LingMain Features of Bot-Depth

• Depth Completion: In scenes where depth sensors fail or data is missing (such as transparent glass and reflective surfaces), it can accurately fill depth holes and generate complete depth maps.
• Monocular Depth Estimation: Predicts depth information from RGB images, providing reliable three-dimensional structure even without a depth sensor.
• Supports Robotic Manipulation: Provides robots with precise depth perception, enabling them to stably grasp transparent or reflective objects in complex environments.
• 3D Tracking and Mapping: Provides high-quality depth data for camera tracking and 3D point tracking, improving the accuracy and stability of trajectory estimation.
• Enhances Vision Tasks: Provides more accurate geometric information for downstream tasks (such as scene understanding and 3D mapping), improving overall performance.

LingTechnical Principles of Bot-Depth

• Natural Mask Learning: Treats naturally missing depth regions in RGB-D cameras as masks, rather than simply treating them as noise. The model learns to reconstruct missing depth from RGB images and residual depth information.
• Dual-Path Dataset: Combines real-world collected data and synthetic simulation data to simulate the imaging artifacts of real sensors and cover various complex scenes.
• ViT Architecture Optimization: Uses a ViT-Large encoder to embed and process RGB and depth information separately, preserving spatial consistency while introducing modality embeddings to distinguish different modalities.
• Adaptive Masking Strategy: Dynamically adjusts the masking ratio according to the completeness of depth data, preserving valuable information and allowing the model to learn in real, complex scenes.
• Cross-Modal Alignment: Through dual alignment of explicit geometric and implicit feature representations, it tightly couples RGB appearance information with depth geometric structure, supporting a variety of downstream tasks.

LingBot-Depth Project Address

• Project Website：https://technology.robbyant.com/lingbot-depth
• GitHub Repository：https://github.com/Robbyant/lingbot-depth
• HuggingFace Model Library：https://huggingface.co/robbyant/lingbot-depth
• Technical Paper：https://github.com/Robbyant/lingbot-depth/blob/main/tech-report.pdf

LingApplication Scenarios of Bot-Depth

• Indoor Mapping and Navigation: In scenarios such as indoor robot navigation and home automation, LingBot-Depth can generate high-precision three-dimensional maps, helping robots better understand spatial structure and plan paths.
• Complex Scene Reconstruction: In fields such as architecture and archaeology, it is used to reconstruct three-dimensional models of complex environments, providing accurate depth information in scenes containing complex materials such as glass and mirrors.
• Virtual Scene Fusion: In AR/VR applications, it provides more accurate depth perception, helping virtual objects better blend with the real environment and improving the user experience.
• Automated assembly: In automated production lines, it helps robots more accurately identify and assemble components with complex shapes, improving production efficiency and quality.
• Household robots: In home environments, it helps robots better perceive the environment, avoid collisions, and improve task completion.