Christian Laforte

@chrlaf.bsky.social

1365 Followers 1459 Following

ML Engineer at NVIDIA. Previously: Stealth GPU startup; Stability AI; AMD; Autodesk; CEO of 2 startups (3D + AI). Toronto, Canada

A resume for a computer scientist, that looks like an advertisement for “my little pony”
Google search for “elon musk named after nazi scifi”.
The "You Win!" screen with my final score, that exceeded 25 millions in one hand.
Young pawpaw tree that bloomed in early May
Graph of 6 benchmarks where generally, LLaDA 8B base beats LLaMA2 7B base, and is on par with LLaMA3 8B Base
an NVIDIA banner in white and green. "I can't wait to see what's next" is written on it.
Fig. 7. Side-by-side comparison. We compare the final inverse rendering of SDF Convolution (8 auxiliary spp), SDF Reparameterization, and our method using the same optimization setup: in total we run 5000 iterations optimizing 50 views; in each iteration, we optimize a batch of 5 views, rendered with 512 × 512 resolution and 64 spp. In all test cases, our method results in comparable or more accurate reconstructions.
[impressive 3D reconstruction of a plan with thin branches and leaves, with fine details] Figure official description: The jump discontinuities at the visibility boundary B make physically based rendering non-differentiable. To explicitly sample and address such discontinuities, we propose to relax the visibility boundary to a thin band of relaxed boundary A. This corresponds to relaxing the surfaces to thin volumes for sampling discontinuities but retaining surface representations for forward rendering. Consequently, we are able to compute accurate gradients of the rendered images with respect to the scene parameters and, in turn, achieve high-quality inverse rendering results, with less complexity than previous methods.
Figure 1 shows inputs from point clouds with lots of holes, and outputs where the holes were filled with plausible 3D reconstruction
Figure 2. Overview of SAMURAI visual object tracker
Figure 2. An overview pipeline of SAMPart3D. (a) We first pre-train 3D backbone PTv3-object on 3D large-scale data Objaverse, distilling visual features from FeatUp-DINOv2. (b) Next, we train light-weight MLPs to distill 2D masks to scale-conditioned grouping. (c) Finally, we cluster the feature of point clouds and highlight the consistent 2D part area with 2D-3D mapping on multi-view renderings, and then query semantics from MLLMs
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