condition-action-denoising-raw-3d-semantic-point-field TEC-0107 · proposedCondition action denoising on a raw 3D semantic point field
Fuse frozen 3D-foundation-model features into VLM visual tokens, and let action hidden states cross-attend (with 3D RoPE) to an unencoded point-wise semantic field, inserted only in the diffusion blocks where linear probing shows degraded e
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Fuse frozen 3D-foundation-model features into VLM visual tokens, and let action hidden states cross-attend (with 3D RoPE) to an unencoded point-wise semantic field, inserted only in the diffusion blocks where linear probing shows degraded e Filed by the drafting stage from Bridge3D: Enabling Vision-Language-Action Models to See and Act in 3D (2609.24525) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Success rate on 10 RoboTwin 2.0 tasks (100 trials each, clean and randomized) against pi-0, Spatial Forcing, PointVLA, 3D Diffusion Policy; ablations removing each strategy (78.8 -> 90.4 -> 92.0 -> 95.6 avg), layer-position ablation, PointNet++ encoder ablation, and six real-robot tasks (RDT 34.3 vs Bridge3D 49.0 avg).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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