FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling
Updated 2 h ago · first seen 11 Sept 2026
paper_01M294H40NRYMGTPV4956R5QAS
- Published
- 11 Sept 2026
- T1 · 2 h ago
- arXiv
- 2506.07350
- T1 · 2 h ago
- Category
- cs.RO
- T1 · 2 h ago
Abstract
-cross Abstract: Semantic map completion, which predicts the layout of unobserved regions from partial observations, is a critical capability for indoor robot navigation. Existing approaches either rely on high-dimensional discrete codebooks that inflate memory, or on iterative diffusion sampling that is too slow for real-time use. We present FastMap, a lightweight two-stage framework for completing top-down categorical semantic maps. First, a lookup-free BitVAE exploits the inherently binary (one-hot) structure of semantic maps to compress each map patch into compact bitwise tokens, yielding a 0.41GB model that is 3.7 times smaller than the prior masked-modeling baseline. Second, a Masked AutoEncoder (MAE)-style transformer reconstructs missing tokens in a single forward pass at 0.011s/map. To support object goal navigation, we additionally introduce an object-aware masking strategy that masks the target category during training and conditions generation on a learnable category embedding, without adding inference cost. On the Gibson benchmark, FastMap achieves 34.10% mIoU and 45.84% semantic Success Rate (sSR), more than doubling the previous best (21.88%), and reaches 83.8% Success Rate on downstream ObjectNav, the highest among the compared navigation methods.
Authors 5
Yijie Deng, Shuaihang Yuan, Congcong Wen, Hao Huang, Anthony Tzes
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
- Arxiv announce type
- replace
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
- arXiv id
- 2506.07350
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
- Categories
- cs.RO, cs.CV
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
- Primary category
- cs.RO
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
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Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
2 h ago
Conflicts
None
No models linked to this paper yet.
- Authors
- Yijie Deng, Shuaihang Yuan, Congcong Wen
As of
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| -cross Abstract: Semantic map completion, which predicts the layout of unobserved regions from partial observations, is a critical capability for indoor robot navigation. Existing approaches either rely on high-dimensional discrete codebooks that inflate memory, or on iterative diffusion sampling that is too slow for real-time use. We present FastMap, a lightweight two-stage framework for completing top-down categorical semantic maps. First, a lookup-free BitVAE exploits the inherently binary (one-hot) structure of semantic maps to compress each map patch into compact bitwise tokens, yielding a 0.41GB model that is 3.7 times smaller than the prior masked-modeling baseline. Second, a Masked AutoEncoder (MAE)-style transformer reconstructs missing tokens in a single forward pass at 0.011s/map. To support object goal navigation, we additionally introduce an object-aware masking strategy that masks the target category during training and conditions generation on a learnable category embedding, without adding inference cost. On the Gibson benchmark, FastMap achieves 34.10% mIoU and 45.84% semantic Success Rate (sSR), more than doubling the previous best (21.88%), and reaches 83.8% Success Rate on downstream ObjectNav, the highest among the compared navigation methods. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CV | feed | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.