Online Segmentation of LiDAR Sequences: Dataset and Algorithm

Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles, driving the need for real-time processing of 3D point sequences. However, most LiDAR semantic segmentation datasets and algorithms split these acquisitions into $360^\circ$ frames, leading to acquisition latency that is incompatible with realistic real-time applications and evaluations. We address this issue with two key contributions. First, we introduce HelixNet, a $10$ billion point dataset with fine-grained labels, timestamps, and sensor rotation information that allows an accurate assessment of real-time readiness of segmentation algorithms. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR point sequences. Helix4D operates on acquisition slices that correspond to a fraction of a full rotation of the sensor, significantly reducing the total latency. We present an extensive benchmark of the performance and real-time readiness of several state-of-the-art models on HelixNet and SemanticKITTI. Helix4D reaches accuracy on par with the best segmentation algorithms with a reduction of more than $5\times$ in terms of latency and $50\times$ in model size. Code and data are available at: https://romainloiseau.fr/helixnet

Representing Shape Collections with Alignment-Aware Linear Models

In this paper, we revisit the classical representation of 3D point clouds as linear shape models. Our key insight is to leverage deep learning to represent a collection of shapes as affine transformations of low-dimensional linear shape models. Each linear model is characterized by a shape prototype, a low-dimensional shape basis and two neural networks. The networks take as input a point cloud and predict the coordinates of a shape in the linear basis and the affine transformation which best approximate the input. Both linear models and neural networks are learned end-to-end using a single reconstruction loss. The main advantage of our approach is that, in contrast to many recent deep approaches which learn feature-based complex shape representations, our model is explicit and every operation occurs in 3D space. As a result, our linear shape models can be easily visualized and annotated, and failure cases can be visually understood. While our main goal is to introduce a compact and interpretable representation of shape collections, we show it leads to state of the art results for few-shot segmentation.