We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with two unprecedented capabilities in LiDAR generative modeling: (i) LiDAR generation guided by driving scenarios, offering significant potential for autonomous driving simulations, and (ii) 4D LiDAR point cloud generation, enabling the creation of realistic and temporally coherent sequences. At the heart of our model is a novel integrated 4D world generation framework. Specifically, we employ latent diffusion models to generate the 3D scene, combine it with dynamic actors to form the underlying 4D world, and subsequently produce realistic sensory observations within this virtual environment. Our experiments indicate that our approach outperforms competing algorithms in realism, temporal coherency, and layout consistency. We additionally show that LidarDM can be used as a generative world model simulator for training and testing perception models. We release our source code and checkpoints at https://github.com/vzyrianov/LidarDM

Despite tremendous advancements in bird’s-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). In this work, we introduce MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism and uncertainty awareness. We evaluate our model on the large-scale nuScenes benchmark. At the time of submission, MapPrior outperforms the strongest competing method, with significantly improved MMD and ECE scores in camera- and LiDAR-based BEV perception. Furthermore, our method can be used to perpetually generate layouts with unconditional sampling.

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over 60 gaze points per second without data loss. Unfortunately, higher data rates are more desirable as they allow for finer granularity and more accurate study analyses. To alleviate this technological problem, a novel method for eye tracking data collection is presented. Instead of performing gaze analysis in real time, all telemetry (keystrokes, mouse movements, and eye tracker output) data during a study is recorded as it happens. Sessions are then replayed at a much slower speed allowing for ample time to map gaze point positions to the appropriate file, line, and column to perform additional analysis. A description of the method and corresponding tool, Déjà Vu, is presented. An evaluation of the method and tool is conducted using three different eye trackers running at four different speeds (60Hz, 120Hz, 150Hz, and 300 Hz). This timing evaluation is performed in Visual Studio and Eclipse IDEs. Results show that Déjà Vu can playback 100% of the data recordings, correctly mapping the gaze to corresponding elements, making it a well-founded and suitable post processing step for future eye tracking studies in software engineering.

A lightweight pointer-analysis framework, srcPtr, is presented to support the implementation and comparison of points-to analysis algorithms. It differentiates itself from existing tools by performing the analysis directly on the abstract syntax tree, as opposed to an intermediate representation (e.g., LLVM IR), by using srcML, an XML representation of source code. Working with srcML and the abstract syntax allows easy access to the actual source code as the programmer views it, thus better supporting comprehension. Currently the framework provides example implementations for both Andersen's and Steensgaard's pointer-analysis algorithms. It also allows for easy integration of other points-to algorithms for comparison of accuracy/speed. The approach is very scalable and can generate pointer dependencies for a 750 KLOC program in less than a minute.