To this end, we propose a novel network named SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels.
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.
In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series.
Ranked #29 on Real-Time Object Detection on COCO
A key challenge in making such methods applicable to articulated objects, such as the human body, is to model the deformation of 3D locations between the rest pose (a canonical space) and the deformed space.
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer.
The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.
Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs.