9 accepted papers
Conventional 3D instance segmentation methods rely on labor-intensive 3D annotations for supervised training, which limits their scalability and generalization to novel objects. Recent approaches leverage multi-view 2D masks from the Segment Anything Model (SAM) to guide the merging of 3D geometric
Point cloud single object tracking is critical in autonomous driving. However, current methods heavily rely on frame-by-frame human annotations, which do not scale well with the growing amount of unlabeled LiDAR data. In this paper, we propose the first self-supervised point cloud single object trac…
Semantic Scene Completion (SSC) is a task that simultaneously predicts the occupancy and semantic labels of the environment. Compared with separate processing, SSC leverages the coupled nature of scene completion and semantic segmentation. Although this multitask integration can utilize complementar
Freespace detection plays an important role in autonomous driving. In recent years, deep learning based freespace detection methods have performed well in urban scenes. However, for off-road scenes, freespace detection poses significant challenges due to the complexity of the scenes and the lack of
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To…
In this work, we propose a novel paradigm to design a hybrid thermal/EO (Electro-Optical or visible-light) camera, whose thermal and RGB frames are pixel-wisely aligned and temporally synchronized. Compared with the existing schemes, we innovate in three ways in order to make it more compact in dime…
In this paper, we propose a road detection method with LiDAR-camera fusion in a novel conditional random field (CRF) framework to exploit both range and color information. In the LiDAR based part, a fast height-difference based scanning strategy is applied in the 2D LiDAR range-image domain and a de…
In this paper, we propose a two-view fusion based convolutional neural network to estimate road areas in urban environments with LiDAR point clouds as input only. The proposed network takes two transformed LiDAR data representations, the LiDAR imageries and the camera-perspective maps, as inputs. It…
This paper proposes a new method for detecting a road from a stereo pair of images. First, the horizon is accurately estimated by a robust, weighted-sampling RANSAC-like method in the improved v-disparity map. The vanishing point of the road region is located using both the horizon information and r…