Transformer开始构建三维世界：开源模型几张图片秒级生成可探索3D场景
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TL;DR - Chinese startup InSpatio (影溯) open-sourced QuerySplat/Topos-Lite, a feed-forward 3D Gaussian Splatting model that turns a handful of uncalibrated phone photos into an explorable 3D scene in seconds, and shipped it as a free consumer app ("Crystal 空间相机"). It matters because it removes per-scene optimization and camera-pose requirements, pushing 3D content creation toward a reusable, scalable capability.
- Replaces the common pixel-aligned 3DGS paradigm with a query-based design: learnable queries act as scene-level "slots" that aggregate information across views and decode Gaussian primitives directly in continuous 3D space, avoiding cross-view pixel conflicts (ghosting, floaters, structural drift).
- Reported SOTA on the DL3DV-Evaluation benchmark: best PSNR and SSIM across 2/4/12-view settings, and top LPIPS in the 2- and 4-view settings; in the 4-view setting it ranks first on all three metrics.
- Single forward pass, sparse input, no camera poses required — shifting cost structure from per-scene optimization to one-time training, with claimed second-level generation latency.
- Explicit capacity-scaling knobs: query count scaled 1,024 → 8,192 during training, plus larger geometry backbone, decoder, and higher-resolution appearance features; the authors stop short of claiming a proven "3D scaling law."
- Released as project page, GitHub weights, and a technical report (arXiv link cited in the article); team led by Prof. 章国锋 with ~20 years of SLAM/3D vision background, framing this as a step toward spatial intelligence and world models for robotics.
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Transformer开始构建三维世界：开源模型几张图片秒级生成可探索3D场景
TL;DR - Chinese startup InSpatio (影溯) open-sourced QuerySplat/Topos-Lite, a feed-forward 3D Gaussian Splatting model that turns a handful of uncalibrated phone photos into an explorable 3D scene in seconds, and shipped it as a free consumer app ("Crystal 空间相机"). It matters because it removes per-scene optimization and camera-pose requirements, pushing 3D content creation toward a reusable, scalable capability.
- Replaces the common pixel-aligned 3DGS paradigm with a query-based design: learnable queries act as scene-level "slots" that aggregate information across views and decode Gaussian primitives directly in continuous 3D space, avoiding cross-view pixel conflicts (ghosting, floaters, structural drift).
- Reported SOTA on the DL3DV-Evaluation benchmark: best PSNR and SSIM across 2/4/12-view settings, and top LPIPS in the 2- and 4-view settings; in the 4-view setting it ranks first on all three metrics.
- Single forward pass, sparse input, no camera poses required — shifting cost structure from per-scene optimization to one-time training, with claimed second-level generation latency.
- Explicit capacity-scaling knobs: query count scaled 1,024 → 8,192 during training, plus larger geometry backbone, decoder, and higher-resolution appearance features; the authors stop short of claiming a proven "3D scaling law."
- Released as project page, GitHub weights, and a technical report (arXiv link cited in the article); team led by Prof. 章国锋 with ~20 years of SLAM/3D vision background, framing this as a step toward spatial intelligence and world models for robotics.