LangSplat, an innovative 3D language Gaussian model jointly developed by Tsinghua University and Harvard University, has achieved a significant breakthrough in the field of 3D spatial language search. It performs open language searches efficiently and accurately 199 times faster than existing LERF methods. The model learns features through visualization, accurately captures object boundaries, and can more accurately identify various parts and ingredients of objects, such as the various ingredients in a bowl of ramen. Its test results on both the LERF data set and the 3D OVS data set prove its superior performance.
LangSplat is an innovative 3D linguistic Gaussian model developed by researchers at Tsinghua University and Harvard University. The model enables efficient and accurate open language search in 3D space, which is 199 times faster than the previous LERF method. The researchers learned features through visualization and successfully captured object boundaries while demonstrating higher accuracy in testing. Not only is LangSplat fast, it can more accurately label parts and ingredients of objects, such as the various ingredients in a bowl of ramen soup. In tests, LangSplat demonstrated superior speed and accuracy on both the LERF dataset and the 3D OVS dataset, bringing new breakthroughs to the field of 3D language search.
The emergence of LangSplat has brought new possibilities to 3D language search technology. Its efficiency and accuracy are expected to be applied in many fields and promote the development and progress of related technologies. Future development is worth looking forward to.