13 accepted papers
We present DecoupledGaussian, a novel system that decouples static objects from their contacted surfaces captured in-the-wild videos, a key prerequisite for realistic Newtonian-based physical simulations. Unlike prior methods focused on synthetic data or elastic jittering along the contact surface,…
Radar hits reflect from points on both the boundary and internal to object outlines. This results in a complex distribution of radar hits that depends on factors including object category, size and orientation. Current radar-camera fusion methods implicitly account for this with a black-box neural n…
As a direct depth sensor, radar holds promise as a tool to improve monocular 3D object detection, which suffers from depth errors, due in part to the depth-scale ambiguity. On the other hand, leveraging radar depths is hampered by difficulties in precisely associating radar returns with 3D estimates…
Despite radar's popularity in the automotive industry, for fusion-based 3D object detection, most existing works focus on LiDAR and camera fusion. In this paper, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. Our TransCAR consists of two modules. T…
Multiple object tracking (MOT) is a critical module for enabling autonomous vehicles to achieve safe planing and navigation in cluttered environments. In tracking-by-detection systems, there are inevitably many false positives and misses among learning-based input detections. The challenge for MOT i…
Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearin…
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with…
While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to the sparsity of radar, but also because automotive radar beams are much wider than a typical pixel combined with a large…
There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video. However, it has been surprisingly difficult to train networks to effectively use both modalities in a way that demonstrates gain over single-modality networks. In thi…
Precision livestock farming uses artificial intelligence to individually monitor livestock activity and health. Tracking individuals over time can reveal health indicators that correlate with productivity and longevity. For instance, locomotion patterns observed in lame pigs have been shown to corre…
While Lidar beams are often represented as rays, they actually have finite beam width and this width impacts the measured shape and size of objects in the scene. Here we investigate the effects of beam width on measurements of thin objects such as vertical poles. We propose a model for beam divergen…
Depth completion involves estimating a dense depth image from sparse depth measurements, often guided by a color image. While linear upsampling is straight forward, it results in depth pixels being interpolated in empty space across discontinuities between objects. Current methods use deep network…
Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D point-cloud-based maps enable precise localization, they impose significant storage and transmission burdens when used in city-scale environments. In this paper, we prop…