SAGO achieves setup-free interactive 3D Gaussian segmentation by modeling it as an online NBV planning task in a Markov process, delivering sub-second latency and over 50x speedup over prior setup-free methods.
In: International conference on machine learning
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FlexDepth proposes a scale-driven family of self-supervised MDE models with two-stage training and SDD decoder claiming SOTA performance and low compute on driving benchmarks.
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Online Segment 3D Gaussians via Launching Virtual Drones
SAGO achieves setup-free interactive 3D Gaussian segmentation by modeling it as an online NBV planning task in a Markov process, delivering sub-second latency and over 50x speedup over prior setup-free methods.
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Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation
FlexDepth proposes a scale-driven family of self-supervised MDE models with two-stage training and SDD decoder claiming SOTA performance and low compute on driving benchmarks.