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In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different LiDAR sensors. To overcome this shortcoming, we propose an unsupervised domain adaptation framework that leverages unlabeled target domain data for self-supervision, coupled with an unpaired mask transfer strategy to mitigate the impact of domain shifts. Furthermore, we introduce gated adapter modules with a small number of parameters into the network to account for target domain-specific information. Experiments adapting from both real-to-real and synthetic-to-real LiDAR semantic segmentation benchmarks demonstrate the significant improvement over prior arts.

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Abstract. Typical methods for camera calibration and image rectification from a single view assume the existence of straight parallel lines from which vanishing points can be computed, or orthogonal structure known to exist in the scene. However, there are practical situations where these assumptions do not apply. Moreover, from a single family of parallel lines on the ground plane there is insufficient information to recover a complete rectification. Here we study a generalization of these methods to scenes known to contain parallel curves. Our method is based on establishing an association between pairs of corresponding points lying on the image projection of these curves. We show how this method can be used to compute a least-squares estimate of the focal length and the camera pose from the tangent lines of the associated points, allowing complete rectification of the image. We evaluate the method on highway and sports track imagery, and demonstrate its accuracy relative to a state-of-the-art vanishing point method.

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In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets. Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. In this paper, we investigate this problem from the data perspective instead of detector architecture design. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges without modifying the detector architecture and without data augmentation. We propose a modelfree point cloud density adjustment pre-processing mechanism that uses iterative MCMC optimization to estimate optimal parameters for altering the point density at different distance ranges. We conduct experiments using four state-of-the-art LiDAR 3D object detectors on two public LiDAR datasets, namely Waymo and ONCE. Our results demonstrate that our range-based point cloud density manipulation technique can improve the performance of the existing detectors, which in turn could potentially inspire future detector designs.