Representing 3D scenes from multiview images remains a core challenge in computer vision and graphics, requiring both reliable rendering and reconstruction, which often conflicts due to the mismatched prioritization of image quality over precise …

${ROOT}/playground ├── pose_kpt # A component for gSDF which solves pose estimation problem ├── hsdf_osdf_1net # The SDF network with a single backbone like Grasping Field or AlignSDF ├── hsdf_osdf_2net # The SDF network with two backbones like gSDF ├── hsdf_osdf_2net_pa # Compared with hsdf_osdf_2net, it additionally uses pixel-aligned visual features ├── hsdf ...

Overview of Dual-branch Guidance. Our dual-branch framework includes a GS-branch for rendering and an SDF-branch for learning neural surfaces. This design preserves the efficiency and fidelity of Gaussian primitive for rendering while accurately approximating scene surfaces from an SDF field adapted from NeuS.

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[NeurIPS 2024]GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction - GSDF/train.py at main · city-super/GSDF

2024年3月25日 · GSDF is introduced, a novel dual-branch architecture that combines the benefits of a flexible and efficient 3D Gaussian Splatting (3DGS) representation with neural Signed Distance Fields (SDF), to leverage and enhance the strengths of each branch while alleviating their limitation through mutual guidance and joint supervision. Presenting a 3D scene from multiview images remains a core and long ...

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2013年10月1日 · Data-Flow models are attracting renewed attention because they lend themselves to efficient mapping on multi-core architectures. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs (SDFGs) onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms with no guarantee of global ...

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