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Works414 from public data
- Deeper Depth Prediction with Fully Convolutional Residual Networks2,056
A fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps is proposed and a novel way to efficiently learn feature map up-sampling within the network is presented.
- Unique Signatures of Histograms for Local Surface Description1,653
A novel comprehensive proposal for surface representation is formulated, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor.
- SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again1,042
A novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot that competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets.
- SHOT: Unique signatures of histograms for surface and texture description1,012
A thorough experimental evaluation vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval.
- Neural Fields in Visual Computing and Beyond866
A review of the literature on neural fields shows the breadth of topics already covered in visual computing, both historically and in current incarnations, and highlights the improved quality, flexibility, and capability brought by neural field methods.
- CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction788
A method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM, based on a scheme that privileges depth prediction in image locations where monocularSLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
- BOP: Benchmark for 6D Object Pose Estimation571
A benchmark for 6D pose estimation of a rigid object from a single RGB-D input image shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methodsbased on 3D local features.
- Performance Evaluation of 3D Keypoint Detectors472
A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures in terms of repeatability, distinctiveness and computational efficiency.
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- Unique shape context for 3d data description383
This paper shows how to deploy a unique local Reference Frame to improve the accuracy and reduce the memory footprint of the well-known 3D Shape Context descriptor.
- 3D Point Capsule Networks371
3D capsule networks are proposed, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data and enables new applications such as part interpolation and replacement.
- Learning 3D Semantic Scene Graphs From 3D Indoor Reconstructions364
This work proposes a learned method that regresses a scene graph from the point cloud of a scene, based on PointNet and Graph Convolutional Networks, and introduces 3DSSG, a semiautomatically generated dataset, that contains semantically rich scene graphs of 3D scenes.
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- Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation293
A 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting that generalizes well to previously unseen input data, and delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects is presented.
- SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences290
This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames by means of a graph neural network and proposes a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario.
- RIO: 3D Object Instance Re-Localization in Changing Indoor Environments283
A new data-driven approach that efficiently finds matching features using a fully-convolutional 3D correspondence network operating on multiple spatial scales is introduced, which outperforms state-of-the-art baselines on the authors' newly-established benchmark, achieving an accuracy of 30.58%.
- SPARF: Neural Radiance Fields from Sparse and Noisy Poses274
This work introduces Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images with noisy camera poses, and sets a new state of the art in the sparse-view regime on multiple challenging datasets.
- SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation273
This paper introduces the largest multi-task synthetic dataset for autonomous driving, SHIFT, which presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density.
- Implicit Neural Representations for Image Compression249
This work proposes the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding and finds that the approach to source compression with INRs vastly outperforms similar prior work, is competitive with common compression algorithms designed specifically for images and closes the gap to state-of-the-art learned approaches based on Rate-Distortion Autoencoders.
- ZNCC-based template matching using bounded partial correlation244
The algorithms shown in this paper generalize and extend the concept of Bounded Partial Correlation, previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC), previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC).
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- Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses232
This work proposes a frame-work for reformulating existing single-prediction models as multiple hypothesis prediction (MHP) models and an associated meta loss and optimization procedure to train them, and finds that MHP models outperform their single-hypothesis counterparts in all cases and expose valuable insights into the variability of predictions.
- Segmentation-Based Adaptive Support for Accurate Stereo Correspondence221
A novel support aggregation strategy which includes information obtained from a segmentation process is proposed which is effective in improving the state of the art in dense stereo correspondence.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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