Guided Gaussians : Enhancing 3D Occupancy Estimation with Sparse Sensor Priors
We introduce a new initialization method for 3D Gaussians used in 3D occupancy estimation, a key task in autonomous driving that involves identifying semantic elements in a vehicle's surroundings and accurately locating them in space. Our approach leverages distance sensor data, such as from lidar or radar, to place 3D Gaussians using farthest point sampling, ensuring coverage of meaningful scene
