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Found 15 papers, 6 papers with code
Compact Neural Graphics Primitives with Learned Hash Probing
Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid.
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering.
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object.
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene.
A primer on perpetuals
When asset prices can jump and the volatility process is independent of the underlying risky assets, we derive an explicit replication strategy for the short side of a perpetual contract.
Variable Bitrate Neural Fields
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations.
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).
Ranked #1 on
Novel View Synthesis
on RTMV
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
Replicating Monotonic Payoffs Without Oracles
In this paper, we show that any monotonic payoff can be replicated using only liquidity provider shares in constant function market makers (CFMMs), without the need for additional collateral or oracles.
Extracting Triangular 3D Models, Materials, and Lighting From Images
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Ranked #2 on
Depth Prediction
on Stanford-ORB
Optimal Fees for Geometric Mean Market Makers
Trading fees have been proposed as a mechanism for compensating LPs for arbitrage losses.
Replicating Market Makers
We present a method for constructing Constant Function Market Makers (CFMMs) whose portfolio value functions match a desired payoff.
When does the tail wag the dog? Curvature and market making
We show that this definition is tightly related to the curvature of a CFMM's trading function and can be used to explain a number of heuristic results.
Why Stake When You Can Borrow?
As smart contract platforms autonomously manage billions of dollars of capital, quantifying the portfolio risk that investors engender in these systems is increasingly important.
Liquidity Provider Returns in Geometric Mean Markets
These strategies allow LP shares to replicate various trading strategies and financial contracts, including standard options.
