SAM4D: Segment Anything in Camera and LiDAR Streams
We present SAM4D, a multi-modal and temporal foundation model designed for
promptable segmentation across camera and LiDAR streams. Unified Multi-modal
Positional Encoding (UMPE) is introduced to align camera and LiDAR features in
a shared 3D space, enabling seamless cross-modal prompting and interaction.
Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA),
which leverages ego-motion compensation to enhance temporal consistency and
long-horizon feature retrieval, ensuring robust segmentation across dynamically
changing autonomous driving scenes. To avoid annotation bottlenecks, we develop
a multi-modal automated data engine that synergizes VFM-driven video masklets,
spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This
framework generates camera-LiDAR aligned pseudo-labels at a speed orders of
magnitude faster than human annotation while preserving VFM-derived semantic
fidelity in point cloud representations. We conduct extensive experiments on
the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal
segmentation ability and great potential in data annotation of proposed SAM4D.
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Jianyun Xu
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Song Wang
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2025-06-27T08:56:59.774Z
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Song Wang
user
songw-zju
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Ziqian Ni
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Chunyong Hu
685e004071131fa43be08ab6
Sheng Yang
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Jianke Zhu
685e004071131fa43be08ab8
Qiang Li
SAM4D: Segment Anything in Camera and LiDAR Streams
We present SAM4D, a multi-modal and temporal foundation model designed for
promptable segmentation across camera and LiDAR streams. Unified Multi-modal
Positional Encoding (UMPE) is introduced to align camera and LiDAR features in
a shared 3D space, enabling seamless cross-modal prompting and interaction.
Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA),
which leverages ego-motion compensation to enhance temporal consistency and
long-horizon feature retrieval, ensuring robust segmentation across dynamically
changing autonomous driving scenes. To avoid annotation bottlenecks, we develop
a multi-modal automated data engine that synergizes VFM-driven video masklets,
spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This
framework generates camera-LiDAR aligned pseudo-labels at a speed orders of
magnitude faster than human annotation while preserving VFM-derived semantic
fidelity in point cloud representations. We conduct extensive experiments on
the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal
segmentation ability and great potential in data annotation of proposed SAM4D.
SAM4D is a multi-modal and temporal foundation model for segmentation in autonomous driving using Unified Multi-modal Positional Encoding and Motion-aware Cross-modal Memory Attention, with a multi-modal automated data engine generating pseudo-labels.
multi-modal temporal foundation model promptable segmentation camera LiDAR Unified Multi-modal Positional Encoding shared 3D space cross-modal prompting Motion-aware Cross-modal Memory Attention ego-motion compensation temporal consistency spatiotemporal 4D reconstruction cross-modal masklet fusion pseudo-labels Waymo-4DSeg