浙大 × 字节 × 港中深最新研究：告别高斯堆砌，终结 3D 冗余｜ECCV 2026
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TL;DR - PointSplat, an ECCV 2026 paper from Zhejiang University, ByteDance, and CUHK-Shenzhen, proposes a compact, feed-forward 3D Gaussian Splatting framework for reconstructing humans from sparse multi-view images. It reduces redundant representations by organizing Gaussians around the person rather than independently around each camera view.
- Replaces view-centric prediction, which repeatedly encodes the same subject across views, with a shared human-centric 3D representation.
- Estimates a coarse human proxy and uses ray casting to remove irrelevant or invisible points before Gaussian prediction.
- A Point-Image Transformer fuses multi-view geometry and appearance into Gaussian position, scale, rotation, opacity, and visual attributes.
- Targets compact, generalizable 3D human reconstruction for bandwidth- and compute-sensitive applications such as immersive live streaming.
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浙大 × 字节 × 港中深最新研究：告别高斯堆砌，终结 3D 冗余｜ECCV 2026
TL;DR - PointSplat, an ECCV 2026 paper from Zhejiang University, ByteDance, and CUHK-Shenzhen, proposes a compact, feed-forward 3D Gaussian Splatting framework for reconstructing humans from sparse multi-view images. It reduces redundant representations by organizing Gaussians around the person rather than independently around each camera view.
- Replaces view-centric prediction, which repeatedly encodes the same subject across views, with a shared human-centric 3D representation.
- Estimates a coarse human proxy and uses ray casting to remove irrelevant or invisible points before Gaussian prediction.
- A Point-Image Transformer fuses multi-view geometry and appearance into Gaussian position, scale, rotation, opacity, and visual attributes.
- Targets compact, generalizable 3D human reconstruction for bandwidth- and compute-sensitive applications such as immersive live streaming.