LingBot-Depth - An open-source spatial perception model from AntLingbo Technology

LingWhat is Bot-Depth?

LingBot-Depth is an open-source high-precision spatial perception model from Antminer Technology, specifically designed to solve the challenge of depth perception for robots in scenes with transparent or reflective objects (such as glass). The model uses Masked Depth Modeling (MDM) technology to reconstruct a complete depth map from RGB images and residual depth information. By combining real and synthetic data for training, it significantly improves the quality of depth output in complex scenes. The model excels in tasks such as depth completion, monocular depth estimation, and robot grasping, helping robots better understand their 3D environment and promoting the practical application of embodied intelligence.

LingBot-Depth's main functions

• Deep CompletionIn scenarios where depth sensors fail or data is missing (such as transparent glass or reflective surfaces), it can accurately fill depth holes and generate a complete depth map.
• Monocular Depth EstimationIt can predict depth information through RGB images and provide reliable 3D structures in the absence of a depth sensor.
• Support robot operationIt provides robots with accurate depth perception, enabling them to stably grasp transparent or reflective objects in complex environments.
• 3D tracking and mappingIt provides high-quality depth data for camera tracking and 3D point tracking, improving the accuracy and stability of trajectory estimation.
• Augmented vision tasksIt provides more accurate geometric information for downstream tasks (such as scene understanding and 3D mapping), thereby improving overall performance.

LingBot-Depth's technical principles

• Natural mask learningThe model treats naturally missing depth regions in the RGB-D camera as masks, rather than simply as noise. It learns to reconstruct the missing depth using RGB images and residual depth information.
• Dual-path datasetIt combines real-world data collection with synthetic simulation data to simulate imaging artifacts from real sensors, covering a variety of complex scenarios.
• ViT architecture optimizationThe ViT-Large encoder is used to embed and process RGB and depth information separately, preserving spatial consistency, while introducing modal embedding to distinguish different modes.
• Adaptive masking strategyThe mask ratio is dynamically adjusted based on the integrity of the depth data to retain valuable information and allow the model to learn in real and complex scenarios.
• Cross-modal alignmentBy employing dual alignment of explicit geometry and implicit feature representation, RGB appearance information is tightly coupled with deep geometry, supporting a variety of downstream tasks.

LingBot-Depth project address

• Project official websitehttps://technology.robbyant.com/lingbot-depth
• GitHub repositoryhttps://github.com/Robbyant/lingbot-depth
• HuggingFace model libraryhttps://huggingface.co/robbyant/lingbot-depth
• Technical Papers: https://github.com/Robbyant/lingbot-depth/blob/main/tech-report.pdf

LingApplication scenarios of Bot-Depth

• Indoor mapping and navigationIn scenarios such as indoor robot navigation and home automation, LingBot-Depth can generate high-precision 3D maps, helping robots better understand spatial structures and plan paths.
• Complex scene reconstructionIn fields such as architecture and archaeology, it is used to reconstruct 3D models of complex environments, providing accurate depth information in scenes containing complex materials such as glass and mirrors.
• Virtual scene fusionIn AR/VR applications, it provides more accurate depth perception, helping virtual objects to better blend with the real environment and improve the user experience.
• Automated assemblyIn automated production lines, it helps robots more accurately identify and assemble complex-shaped parts, improving production efficiency and quality.
• Household robotsIn a home environment, this helps robots better perceive their surroundings, avoid collisions, and improve task completion rates.