4k citations
- Nvidia (United States)US7 papers
- Stanford UniversityUS7 papers
- University of TorontoCA7 papers
- University of California, MercedUS6 papers
- University of WashingtonUS5 papers
- Duke UniversityUS4 papers
- Massachusetts Institute of TechnologyUS4 papers
- University of CopenhagenDK4 papers
- Google (United States)US3 papers
- Nanyang Technological UniversitySG3 papers
- University of California, BerkeleyUS3 papers
- University of California, IrvineUS3 papers
40 papers · 1 filter
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière +5
3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when…
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied +1
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that perm…
EditGAN: High-Precision Semantic Image Editing
Huan Ling, Karsten Kreis, Daiqing Li +3
Generative adversarial networks (GANs) have recently found applications in image editing. However, most GAN based image editing methods often require large scale datasets with sema…
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen, Joey Litalien, Jun Gao +5
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers. Many previous learning-based approaches fo…
Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers
Christof Henkel
We present an efficient end-to-end pipeline for largescale landmark recognition and retrieval. We show how to combine and enhance concepts from recent research in image retrieval a…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world imag…