SpatialLM

Author(s): Kalash Vasaniya

Originally published on Towards AI.

Bridging Unstructured 3D Data with Structured InsightsSource: From https://huggingface.co/manycore-research/-Llama-1B

Model: https://huggingface.co/manycore-research/-Llama-1B

is a novel 3D language model that has the ability to use raw 3D point cloud data and transform it into readable and comprehendible descriptions of the scene. was developed by Manycore Research and addresses issues of 3D scene analysis. It achieves this by extracting salient information of buildings and objects, including walls, doors, windows, and oriented bounding boxes, from media such as single-camera video, RGBD images, and LiDAR scans.

Here we present the design, configuration, code execution, and testing of -Llama-1B. We provide a step-by-step user manual on how to use its functionalities.

Source: From https://huggingface.co/manycore-research/-Llama-1B

employs multiple input forms to map unorganized 3D shape data to organized 3D scene outputs. ’s flexibility allows it to work with point clouds obtained from:

Single-camera video clipsRGBD imagesLiDAR sensors

Unlike previous methods that needed specialized devices to collect data, is intended to work with data from general equipment and sensors. This makes spatial reasoning in 3D more convenient for more people.

Spatial thinking lies at the heart of most contemporary applications:

Embodied Robotics: Assists robots in perceiving and engaging with the world.Autonomous Navigation: Assists self-driving vehicles with knowing their location.Advanced 3D Scene Analysis: Enables more detailed AR/VR applications and architecture planning.

… Read the full blog for free on Medium.

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Published via Towards AI