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Found 11 papers, 7 papers with code
CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
CLIP2: Contrastive Language-Image-Point Pretraining From Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving.
Point2Seq: Detecting 3D Objects as Sequences
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.
Voxel Transformer for 3D Object Detection
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.
Ranked #3 on
3D Object Detection
on waymo vehicle
(L1 mAP metric)
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.
Ranked #2 on
3D Object Detection
on waymo vehicle
(AP metric)
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.
One Million Scenes for Autonomous Driving: ONCE Dataset
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal
Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc.
GRNet: Gridding Residual Network for Dense Point Cloud Completion
In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information.
Ranked #1 on
Point Cloud Completion
on Completion3D
Interpolated Convolutional Networks for 3D Point Cloud Understanding
Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs.
Ranked #23 on
3D Part Segmentation
on ShapeNet-Part
