19 accepted papers
LLM agents have achieved strong performance in tool-augmented reasoning, but most remain largely stateless: after each episode, the agent discards interaction traces and does not accumulate reusable strategies. Prior work either stores raw trajectories for case-based reuse or relies on external teac…
The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code. However, existing benchmarks primarily focus on static screenshot-to-code tasks, thereby overlooking the dynamic interactions fundamental to real-world web applications. To addr…
End-to-end autonomous driving systems increasingly rely on vision-centric world models to understand and predict their environment. However, a common ineffectiveness in these models is the full reconstruction of future scenes, which expends significant capacity on redundantly modeling static backgro…
This paper introduces DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating real-world scenarios. DriveArena comprises two core components: Traffic Manager, a traffic simulator capable of generating realistic traffic flow on any global street map, a…
Semantic Scene Completion (SSC) is pivotal in autonomous driving perception, frequently confronted with the complexities of weather and illumination changes. The long-term strategy involves fusing multi-modal information to bolster the system's robustness. Radar, increasingly utilized for 3D target
Traffic simulation, complementing real-world data with a long-tail distribution, allows for effective evaluation and enhancement of the ability of autonomous vehicles to handle accident-prone scenarios. Simulating such safety-critical scenarios is nontrivial, however, from log data that are typicall
Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenarios and causal relationships, hindering adaptability and interpretability in varied environments. To address the above p…
Combining motion prediction in LiDAR-based 3D object detection is an effective method for improving overall accuracy, especially the downstream autonomous driving tasks. The recent development of low-cost LiDARs (e.g. Livox LiDAR) enables us to explore such 4D perception systems with a lower budget…
Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes. Existing offboard methods focus on 3D object detection with closed-set taxonomy and fail to match human-level recognition capability on the rapidly evolving perception tasks. Due to heavy re…
Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient com…
Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively e…
"Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design…
Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robotics. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Further-more, they are usually centralized planners and require the whole state of the…
In this paper, we present PocoNet: Point cloud Online COmpression NETwork to address the task of SLAM-oriented compression. The aim of this task is to select a compact subset of points with high priority to maintain localization accuracy. The key insight is that points with high priority have simila…
Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In t…
Outdoor scene completion is a challenging issue in 3D scene understanding, which plays an important role in intelligent robotics and autonomous driving. Due to the sparsity of LiDAR acquisition, it is far more complex for 3D scene completion and semantic segmentation. Since semantic features can pro…
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems. In this paper, we focus on semantic scene completion, which is a task of jointly estimating the volumetric occupancy and semantic labels of objects. Since the real-world data is sparse and oc…
Due to the difficulty in generating the effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most of the existing methods that focus on extracting local, global, and statistical features of raw point clouds, our…
Most matrix completion methods impose a low-rank prior or its variants to well pose the problem. However, the rank minimization is problematic to handle matrices with structural missing. To remedy this, this paper introduces a new matrix completion method using double priors on the latent matrix, na…