LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+
  FPS
In this paper, we introduce LangSplatV2, which achieves high-dimensional
feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6
FPS for high-resolution images, providing a 42 times speedup and a 47
times boost over LangSplat respectively, along with improved query accuracy.
LangSplat employs Gaussian Splatting to embed 2D CLIP language features into
3D, significantly enhancing speed and learning a precise 3D language field with
SAM semantics. Such advancements in 3D language fields are crucial for
applications that require language interaction within complex scenes. However,
LangSplat does not yet achieve real-time inference performance (8.2 FPS), even
with advanced A100 GPUs, severely limiting its broader application. In this
paper, we first conduct a detailed time analysis of LangSplat, identifying the
heavyweight decoder as the primary speed bottleneck. Our solution, LangSplatV2
assumes that each Gaussian acts as a sparse code within a global dictionary,
leading to the learning of a 3D sparse coefficient field that entirely
eliminates the need for a heavyweight decoder. By leveraging this sparsity, we
further propose an efficient sparse coefficient splatting method with CUDA
optimization, rendering high-dimensional feature maps at high quality while
incurring only the time cost of splatting an ultra-low-dimensional feature. Our
experimental results demonstrate that LangSplatV2 not only achieves better or
competitive query accuracy but is also significantly faster. Codes and demos
are available at our project page: https://langsplat-v2.github.io.
68708cb9c8391850d609788f
Wanhua Li
claimed_verified
2025-07-13T07:26:20.922Z
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Wanhua Li
user
EthanTaylor
68708cb9c8391850d6097890
Yujie Zhao
claimed_verified
2025-08-18T07:00:07.215Z
6721acf7bbf9703bc285e840
/avatars/487f5cae476758d318465aa9ed103568.svg
Yujie Zhao
user
HomieZ
68708cb9c8391850d6097891
Minghan Qin
claimed_verified
2025-07-29T12:51:12.726Z
642002b51ccd411979d72b18
https://cdn-avatars.huggingface.co/v1/production/uploads/642002b51ccd411979d72b18/JO9e0o8fAFNC-eYFbx_JW.png
Minghan Qin
user
Qmh
68708cb9c8391850d6097892
Yang Liu
68708cb9c8391850d6097893
Yuanhao Cai
68708cb9c8391850d6097894
Chuang Gan
68708cb9c8391850d6097895
Hanspeter Pfister
LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+
  FPS
In this paper, we introduce LangSplatV2, which achieves high-dimensional
feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6
FPS for high-resolution images, providing a 42 times speedup and a 47
times boost over LangSplat respectively, along with improved query accuracy.
LangSplat employs Gaussian Splatting to embed 2D CLIP language features into
3D, significantly enhancing speed and learning a precise 3D language field with
SAM semantics. Such advancements in 3D language fields are crucial for
applications that require language interaction within complex scenes. However,
LangSplat does not yet achieve real-time inference performance (8.2 FPS), even
with advanced A100 GPUs, severely limiting its broader application. In this
paper, we first conduct a detailed time analysis of LangSplat, identifying the
heavyweight decoder as the primary speed bottleneck. Our solution, LangSplatV2
assumes that each Gaussian acts as a sparse code within a global dictionary,
leading to the learning of a 3D sparse coefficient field that entirely
eliminates the need for a heavyweight decoder. By leveraging this sparsity, we
further propose an efficient sparse coefficient splatting method with CUDA
optimization, rendering high-dimensional feature maps at high quality while
incurring only the time cost of splatting an ultra-low-dimensional feature. Our
experimental results demonstrate that LangSplatV2 not only achieves better or
competitive query accuracy but is also significantly faster. Codes and demos
are available at our project page: https://langsplat-v2.github.io.
LangSplatV2 improves the speed and accuracy of 3D open-vocabulary text querying by eliminating the need for a heavyweight decoder through Gaussian Splatting and sparse coefficient splatting.
Gaussian Splatting 3D language fields SAM semantics sparse code 3D sparse coefficient field sparse coefficient splatting CUDA optimization