princeton-vl

Infinite Photorealistic Worlds using Procedural Generation

Deep Patch Visual Odometry/SLAM

[ECCV2024 - Oral, Best Paper Award Candidate] SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow

Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"

Pytorch implementation of the ECCV 2016 paper "Stacked Hourglass Networks for Human Pose Estimation"

A Learning Environment for Theorem Proving with the Coq proof assistant

Training code for "Associative Embedding: End-to-End Learning for Joint Detection and Grouping"

Code to test and use the model from "Stacked Hourglass Networks for Human Pose Estimation"

[ICLR2026 - Oral] WAFT: Warping-Alone Field Transforms for Optical Flow

[ECCV 2026] WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching

Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Training code for "Pixels to Graphs by Associative Embedding"

[ICCV 25] Official code for "OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration"

Code for the NIPS 2016 paper

Code for the CVPR 2019 paper "Learning Single-Image Depth from Videos using Quality Assessment Networks"

An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition

Official code for View Synthesis with Sculpted Neural Points

Code for Decorrelated Batch Normalization

[ECCV24] official code for "OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations"

Code for the CVPR 2020 paper "OASIS: A Large-Scale Dataset for Single Image 3D in the Wild"

[ECCV 26 Spotlight] Official code for Zero-Shot Depth from Defocus (https://arxiv.org/abs/2603.26658)

Code for reproducing experiments in "How Useful is Self-Supervised Pretraining for Visual Tasks?"

Code for reproducing results in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"

[ECCV 2024] LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow

Function-Oriented Abstractions for Procedural 3D Generation in Python