SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling
本文提出SpatialDiff方法，通过隐式3D空间建模和全局空间监督解决复杂场景中物体移动不准确的问题。
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GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes
This work addresses the highly underconstrained problem of inserting new furniture into single-view RGB-D indoor scenes, where the absence of unique scale and positional cues complicates 3D placement. To tackle this challenge, the authors propose a two-stage framework: first, a data-driven iterative inference procedure estimates geometrically plausible 3D poses; second, these poses are projected into pixel-aligned 2D constraints to guide the synthesis of geometrically consistent novel views. By reformulating furniture insertion as a joint optimization of 3D pose inference and image generation, the method introduces a generative-oriented 3D pose reasoning mechanism. Experiments demonstrate that the proposed approach outperforms baseline methods such as direct regression in pose estimation and consistently produces visually coherent results that align closely with underlying 3D geometry across varying furniture scales, confirming its geometric plausibility and generative stability.
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SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling
本文提出SpatialDiff方法，通过隐式3D空间建模和全局空间监督解决复杂场景中物体移动不准确的问题。
0 citationsRead paper
GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes
This work addresses the highly underconstrained problem of inserting new furniture into single-view RGB-D indoor scenes, where the absence of unique scale and positional cues complicates 3D placement. To tackle this challenge, the authors propose a two-stage framework: first, a data-driven iterative inference procedure estimates geometrically plausible 3D poses; second, these poses are projected into pixel-aligned 2D constraints to guide the synthesis of geometrically consistent novel views. By reformulating furniture insertion as a joint optimization of 3D pose inference and image generation, the method introduces a generative-oriented 3D pose reasoning mechanism. Experiments demonstrate that the proposed approach outperforms baseline methods such as direct regression in pose estimation and consistently produces visually coherent results that align closely with underlying 3D geometry across varying furniture scales, confirming its geometric plausibility and generative stability.
0 citationsRead paper