Qwen-3D Paper: Solving Long Video Reasoning Bottlenecks via 3D Geometry Compression
Scobleizer · x · 2026-08-08
Qwen-3D, a paper submitted to ECCV 2026, introduces a generalist 3D vision-language model capable of handling grounding, segmentation, VQA, and spatial reasoning simultaneously.
Existing Large Multimodal Models (LMMs) struggle with long videos due to context window limits caused by frame-centric tokenization. The core idea of this paper is to use 3D geometry as a natural compression mechanism: leveraging depth and camera poses to fuse multi-view and temporal observations into a persistent, world-aligned 3D representation.
Furthermore, the model integrates 3D Rotary Positional Embeddings into the Qwen backbone, allowing attention mechanisms to operate directly in 3D scene space. This overcomes the decoding bottleneck between language reasoning and dense geometric prediction in existing methods, enabling efficient cross-view and temporal reasoning.
Related event: Qwen-3D: Enhancing Spatial Reasoning with 3D Geometry(2 posts)→
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