Google has introduced Spatial VLM, a Vision-Language Model with 3D spatial reasoning capabilities. The model aims to enhance understanding and reasoning about spatial relationships, crucial for Visual Question Answering (VQA) and robotics. It investigates the potential of synthetic data in helping VLMs learn 3D relationships, quantitative distance, CoT spatial reasoning, and RL reward. This development addresses the limitations of current VLMs in spatial reasoning, which is essential for embodied agents, policies, and planners. Spatial VLM, trained on 3D data synthesized from web-scale 2D images, outperforms general VLMs on spatial reasoning tasks. The addition of 3D reasoning capabilities to VLMs offers benefits for robotics and AR applications, enabling the answering of quantitative distance questions as a reward signal.
For robotics and AR applications, there’s a lot of benefits of having spatially 3D grounded VLMs. This recent work led by @BoyuanChen0 adds 3D reasoning capabilities to VLMs. One cool result is that we are able to answer *quantitative* distance questions as a reward signal. https://t.co/NVYNT7oGzQ https://t.co/SkgmBAj3QY
VLMs are good at semantic queries, but how well do they understand lower-level spatial relationships? Spatial VLM is trained on 3d data synthesized from web-scale 2d images. It outperforms general VLMs on spatial reasoning tasks. Checkout the thread by @BoyuanChen0 : https://t.co/iHHTxYV6HR
VLMs are good at semantic queries, but how well do they understand lower-level spatial relationships? Spatial VLM is trained on 3d data synthesized from web-scale 2d images. It out performs general VLMs on spatial reasoning tasks. Checkout the thread by @BoyuanChen0: https://t.co/Aj5aZj5DFh https://t.co/iHHTxYV6HR
This is a great development! Current VLMs are really bad about spatial reasoning (they just weren’t trained for it). Yet such capabilities are crucial for any embodied agent. Given the transition to using VLMs as policies/planners, figuring this aspect is a key component. https://t.co/5iWg6YMvxm
Introducing Spatial VLM, a Vision-Language Model with 3D Spatial Reasoning Capabilities by @GoogleDeepmind. We investigate to what extent synthetic data can help VLMs learn - 3D relationship - quantitative distance - CoT spatial reasoning - RL reward https://t.co/e22zrBhKjB (1/6)
Google presents SpatialVLM Endowing Vision-Language Models with Spatial Reasoning Capabilities paper page: https://t.co/PMQWwcNzne Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision… https://t.co/uJceeRfwCB