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Department of Computer Science, City University of Hong Kong
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CanonicalGS achieves a remarkable 2.5 dB improvement in novel view synthesis quality by stabilizing scene representations against noisy input data.
Robots can now recover from falls and seamlessly transition to walking on diverse terrains without prior terrain knowledge, thanks to a novel decoupling of training phases and surface types.
