7 accepted papers
Multimodal large language models (MLLMs) have made significant advancements in vision understanding and reasoning. However, the autoregressive Transformer architecture used by MLLMs requires tokenization on input images, which limits their ability to accurately ground objects within the 2D image spa
3D point cloud object detection plays an important role in autonomous driving. However, labeling 3D object boxes is expensive and time-consuming, limiting the number of annotated point clouds used in fully-supervised training. This has led to a rise in semi-supervised 3D object detection research, w
Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data often suffer from corruptions, such as sensor noise, which violates the benign data assumption in current protocols. As
"Existing 3D semantic occupancy prediction methods typically treat the task as a one-shot 3D voxel-wise segmentation problem, focusing on a single-step mapping between the inputs and occupancy maps, which limits their ability to refine and complete local regions gradually. In this paper, we introduc…
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space where 2D latent representations are mapped and subsequent 3D operators are applied. However operating on dense latent spaces introduces a cubic time and space complexity which limits scalability in terms of pe…
"Perceiving the world as 3D occupancy supports embodied agents to avoid collision with any types of obstacle. While open-vocabulary image understanding has prospered recently, how to bind the predicted 3D occupancy grids with open-world semantics still remains under-explored due to limited open-worl…
Knowledge transfer from multi-modal, i.e., LiDAR points and images, to a single LiDAR modal can take advantage of complimentary information from modal-fusion but keep a single modal inference speed, showing a promising direction for point cloud semantic segmentation in autonomous driving. Recent adv…