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About this paper
Xunlan Zhou, Hongrui Zhao, Negar Mehr
TACO (Temporal Consensus Optimization) presents a replay-free framework for continual neural mapping that balances adaptation to dynamic scene changes with the preservation of valid historical knowledge. This method successfully avoids persistent ghost artifacts and fragmented geometry, outperforming replay-based and regularization-based baselines in dynamic environments while reducing CPU memory usage by over 50% compared to full replay.
Key insights
- Reformulating continual learning as a temporal consensus problem offers a principled way to balance stability and plasticity in dynamic mapping without the memory overhead of data replay.
- Parameter-wise importance estimation, derived from prediction sensitivity, enables selective preservation of reliable historical knowledge while allowing flexible adaptation in changing or uncertain regions.
- A masking mechanism, which suppresses temporal constraints for parameters below a certain importance threshold, improves robustness by filtering out unreliable early-stage gradients and allowing unconstrained adaptation in less reliable areas.
Summary by alphaXiv.
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