LingBot-Depth 2.0 - A spatial perception model launched by Antminer Lingbo Technology - AiBoss

What is LingBot-Depth 2.0?

LingBot-Depth 2.0 is a spatial perception model for real-world scenarios launched by Antminer Lingbo Technology, along with the open-source visual foundation model LingBot-Vision. The training data has been expanded from 3 million to 150 million, achieving first place in 12 out of 16 depth completion benchmarks. The RMSE error in large-area depth-deficient indoor scenes has decreased from 0.132 to 0.062. The model performs exceptionally well in scenes with transparent objects such as glass and mirrors, and has passed Orbbec's professional certification, establishing a capability chain for robots from "understanding" to "accurately perceiving."

Main functions of LingBot-Depth 2.0

Deep CompletionBased on RGB images and sparse depth input, a high-precision dense depth map is generated.
Perception of transparent objectsAchieve complete 3D structural reconstruction for transparent/reflective objects such as glass, mirrors, and faucets.
Fine edge recognitionIt has sub-pixel-level boundary positioning capability, accurately identifying object outlines and small structures.
Timing stabilityMaintain temporal consistency and spatial accuracy of depth estimation in video streams.
Cross-scenario generalizationIt covers realistic scenes including indoor and outdoor environments, complex lighting conditions, and various materials.

Technical Principles of LingBot-Depth 2.0

Dual-model collaborative architectureLingBot-Depth 2.0 is not a single network but adopts a two-level collaborative architecture of "base model + task head". The bottom layer LingBot-Vision is responsible for extracting boundary and spatial structure features from RGB images, while the upper layer LingBot-Depth completes the mapping from sparse or noisy depth to dense depth map based on these features. The decoupled design allows visual representation and depth estimation to focus on their respective strengths, building a complete capability link for the robot from "understanding" to "accurately seeing".
LingBot-Vision: Boundary Structure Pre-trainingLingBot-Vision is the industry's first visual foundation model that uses "boundary structure" as an explicit pre-training target. It uses geometric modeling to learn structural knowledge such as "where the object is, how the edges are distributed, and how the space is organized," enabling the model to have sub-pixel-level boundary localization capabilities. With only 160 million images for pre-training, its clarity and stability in recognizing object contours, surface boundaries, and fine structures are superior to general visual foundation models that rely on billions of images.
Deep completion mechanismThe depth completion network of LingBot-Depth 2.0 is based on the boundary-aware features provided by LingBot-Vision. It identifies depth discontinuities through the encoder and then performs multi-scale feature fusion through the decoder. While maintaining the sharpness of object edges, it performs reasonable interpolation on smooth areas. The key is that the model uses explicit boundary structure features as constraints for depth propagation, which effectively prevents the "depth overflow" problem common in traditional depth completion and ensures that the foreground and background are correctly truncated at the object boundaries. At the same time, a temporal feature alignment mechanism is introduced to ensure the inter-frame depth consistency under the video stream input.

How to use LingBot-Depth 2.0

Get the modelDownload the LingBot-Vision open-source weights from HuggingFace or ModelScope.
Cloning codeGet the code and technical reports via the GitHub repository https://github.com/robbyant/lingbot-vision.
Environment configurationConfigure the dependent environment according to the documentation and prepare RGB-D input data.
Calling inference: Load the model for deep completion inference, or train downstream vision tasks based on LingBot-Vision.
End-side integrationUsing Orbbec Gemini 330 series cameras, integrate LingBot-Depth commercial version capabilities via SDK.

The core advantages of LingBot-Depth 2.0

Leading in data scaleThe training data has reached 150 million, 50 times more than the previous generation, covering a wider range of real-world scenarios.
Excellent test resultsIt achieved first place in 12 out of 16 deep completion benchmark tests.
Error significantly reducedIn indoor scenes with large areas of missing depth, the RMSE error is halved compared to the previous generation.
Breakthrough in transparent scenesIt performs particularly well in scenarios where traditional depth cameras malfunction, such as those involving glass or mirrors.
Hardware certification supportIt has passed the professional certification of Orbbec Deep Vision Lab, and its edge sharpness and robustness have been significantly improved.

The project address for LingBot-Depth 2.0

Project official websitehttps://technology.robbyant.com/lingbot-vision
GitHub repositoryhttps://github.com/robbyant/lingbot-vision
HuggingFace model libraryhttps://huggingface.co/collections/robbyant/lingbot-vision

Comparison of LingBot-Depth 2.0 with similar competing products

Comparison Dimensions	LingBot-Depth 2.0	Depth Anything V2
technical route	Visual pedestal model pre-trained based on boundary structure + depth completion	Discriminant model based on DINOv2 + training with synthetic data and pseudo-labels
Data scale	150 million real-world training data points	595,000 composite images + 62 million pseudo-annotated real images
Core advantages	Transparent/reflective objects, fine edge recognition, robot scene optimization	Monocular depth estimation, multi-scale model, fast inference speed
Open source situation	LingBot-Vision versions four (ViT-G/L/B/S) are now open source.	Four open-source versions (Small is Apache 2.0, the others are CC-BY-NC-4.0).
Industrial landing	In deep collaboration with Orbbec, we launched an SDK and an all-in-one camera.	Primarily academic research and general scenarios

Application Scenarios of LingBot-Depth 2.0

Home service robotsIt enables precise navigation and operation in environments with glass, mirrors, and faucets, such as kitchens and bathrooms.
Industrial quality inspection: Perform three-dimensional dimensional measurement and defect detection on transparent packaging and reflective metal parts.
Warehousing and Logistics: Identify the edges of goods and small objects in complex lighting warehouses to assist robotic arms in grasping them.
autonomous driving: To supplement the depth information of road scenes and improve the perception of transparent obstacles (such as glass curtain walls).
Data acquisition equipmentIntegrated into RGB-D acquisition devices such as Orbbec EGO, it provides accurate real-world data for embodied intelligent training.