AiDe: Improving 3D Open-Vocabulary Semantic Segmentation by Aligned Vision-Language Learning
Bibliographic record
Abstract
3D open-vocabulary semantic segmentation aims at recognizing countless categories beyond the limited set of annotations used in traditional settings. Due to the lack of large-scale 3D-vision-language segmentation data, instead of training models from scratch, the current solutions distill knowledge from pre-trained 2D vision-language models (VLMs) into 3D models. However, this distillation is supervised by misaligned 3D-scene-image-to-text data pairs, consequently leading to suboptimal performance. Moreover, as 2D VLMs are trained on 2D datasets, text encoders of VLMs, which serve as the bridge between 3D models and an unbounded set of categories, lack 3D semantics. In this paper, to address these issues and improve generalization performance, we propose an Aligned 3D Open-Vocabulary SEmantic Segmentation framework, called AiDe, with two novel modules. To collect high-quality and well-aligned 3D-scene-image-to-text pairs, our CLIP-rewarded alignment module (i) generates diverse captions of multi-view images of 3D scenes to capture details by varying the temperatures and then (ii) samples captions based on their similarity to corresponding images for rich and accurate associations. Next, to adapt 2D VLMs to 3D contexts, our adaptive segmentation module introduces (iii) trainable tokens within the input space and each layer of the text encoder, while freezing the text encoder to avoid catastrophic forgetting. Extensive experiments show that AiDe outperforms previous methods by a large margin on three representative benchmarks, demonstrating its effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".